CN107942377A - A kind of seismic data compression and reconstructing method - Google Patents
A kind of seismic data compression and reconstructing method Download PDFInfo
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- CN107942377A CN107942377A CN201810010947.9A CN201810010947A CN107942377A CN 107942377 A CN107942377 A CN 107942377A CN 201810010947 A CN201810010947 A CN 201810010947A CN 107942377 A CN107942377 A CN 107942377A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/288—Event detection in seismic signals, e.g. microseismics
Abstract
The present invention relates to a kind of seismic data compression and reconstructing method, comprise the following steps：First, to seismic data wavelet transformation, the compressibility of seismic data is increased；Then, the achievable calculation matrix of hardware is constructed according to chaos sequence, and the seismic data compression after wavelet transformation is observed with chaotic measurement matrix；Finally, Bayes's wavelet tree structure compresses sensing reconstructing algorithm is improved, complete seismic data is recovered with improved BTSWCS vb algorithms.Real data result of the test of the present invention shows：Compared to common random measurement matrix, the chaotic measurement matrix of the present invention is easy for hard-wired, several chaos sequence matrixes can not only realize seismic data compression than the Real Time Compression for 0.2~0.55, but also also fine to reconstruct influential effect, particularly Logistic sequence matrix；The improved restructing algorithm BTSWCS vb algorithms of the present invention, not only increase reconstruction accuracy, and be obviously shortened reconstitution time.
Description
Technical field
The present invention relates to a kind of processing method of seismic data, more particularly to a kind of seismic data compression and reconstructing method,
Based on Bayes's compressed sensing innovatory algorithm.
Background technology
Recycling in real time for mass data brings huge challenge to existing seismic detector during geological prospecting, this is also to restrict
The main factor of seismic detector development.Particularly, deepening continuously with seismic prospecting so that seismic exploration data continues swollen
It is swollen, caused greatly to seismographic sampling and the processing speed for being wirelessly transferred speed, the memory capacity of memory and computer
Pressure.By the key technology of compressive sensing theory, in slave computer to seismic data Real Time Compression, data transfer to host computer,
When seismic data interpretation is needed host computer again can High precision reconstruction go out complete initial data, this can not only improve number
According to processing speed and reduce memory space, and seismic detector wireless communication data transmission performance can be improved.Therefore, seismic data
Compression and reconstructing method become problem there is an urgent need to research.
CN106772567A is disclosed《A kind of data transfer lossless compression algorithm for seismic prospecting instrument》, the reality of compression
Now it is divided to two aspects：First aspect be according to the first two sampled point to the actual value of the predicted value of this sampled point and this sampled point it
Difference tries to achieve prediction difference Δ V, and 24 initial data of this sampled point are substituted with Δ V；Second aspect is according to data characteristics, using trip
Journey encodes and the instruction data bits section of two second compression indication predicting difference DELTA V data bits of huffman coding, substantially reduces one
In frame indicated number according to digit section length.Which employs more second compressions, compressed capability is strong, it is required upload data volume reduce 50% with
On.This method has in terms of the complexity and timeliness of algorithm, calculates and realizes simply, does not change acquisition station microprocessor reality
The advantages of when property.
CN103067022A is disclosed《A kind of integer data lossless compression method, decompression method and device》, there is provided one
Kind integer data compression method, including：Position reorganization process, data block division and essential information storing process and coded treatment
Process；Integer data decompression method includes：Essential information resolving, decoding process and bit recovery processing procedure；It is whole
Type data compression device includes：Position reorganization module, data block division and essential information memory module and coded treatment module；
Integer data decompressing device includes：Essential information parsing module, decoding process module and bit recovery processing module.This method has
Have that algorithm is simple, is easily achieved, coding and decoding is efficient, and compression effectiveness is good, available for voice data, seismic prospecting signal,
Polytype integer data such as geophysical log Wave data and view data carries out the advantages of lossless compression and decompression.
Disclosed in Wen Mao within 2011《Seismic data compression based on SPIHT innovatory algorithms》, with the algorithm to earthquake number
Effectively compress and reconstruct according to carrying out, while the encoding and decoding time is reduced, higher compression ratio and preferable figure can be obtained
As quality reconstruction.For the validity of verification algorithm, it have chosen 10 width seismic cross-sections and tested, the results showed that the algorithm energy
It is enough to retain the objective quality of image well, while also improve the efficiency of encoding and decoding.Carlos is disclosed within 2015
《Seismic Data Compression Using 2D Lifting-Wavelet Algorithms》, lifted in study two-dimensional
During wavelet compression seismic data, it is found through experiments that the more signal-to-noise ratio of decomposition level are lower, non-uniform quantizing is in amplitude
Shi Rongyi malfunctions, and minimum entropy is obtained with uniform quantization in seismic data application, when target is to realize that signal-to-noise ratio is higher than 40dB
When, Huffman encoding ratio arithmetic codings are more preferable.Demonstrate compression effectiveness and filter type and length, Decomposition order, quantization side
Formula and coding mode are related.
Although the above-mentioned prior art can be used for seismic data compression and reconstruct, in common compression algorithm compression multiple be with
Loss signal accuracy is cost, and level of data compression directly influences the demand of quality reconstruction, Real Time Compression and High precision reconstruction
It can not meet at the same time.And in view of compression process more focuses on real-time, restructuring procedure more focuses on accuracy, and common compression reconstruct is calculated
Method is can not to meet seismic survey system Real Time Compression, and High precision reconstruction goes out the demand of initial data.
The content of the invention
The object of the invention is that in view of the above shortcomings of the prior art, there is provided a kind of seismic data compression and reconstruct side
Method.
The present invention gives the seismic data compression method based on compressive sensing theory first；Then, earthquake number is given
According to reconstructing method.According to compressive sensing theory：If signal is sparse either sparse in some transform domain, then letter
Number original signal can be transformed to low-dimensional signal by calculation matrix, then original signal is obtained by restructing algorithm Optimization Solution.
It can be seen that：The construction of compression method, that is, calculation matrix, the design of reconstructing method, that is, reconstruction and optimization algorithm.It is of the invention and normal
See seismic data compression restructing algorithm difference lies in：Seismic data can be realized and be compressed in collection, compress measurement used
Matrix is easy to hardware realization, and compression process and restructuring procedure are that two independent algorithms are realized, reconstruction accuracy not exclusively by
The restriction of compression ratio size, seismic data can be compressed in slave computer, recycle compressed data, when subsequently needing seismic data
During explanation, then in host computer High precision reconstruction go out complete initial data.
The purpose of the present invention is what is be achieved through the following technical solutions：
A kind of seismic data compression and reconstructing method, comprise the following steps：
A, seismic monitoring real data x is inputted；
B, to input data wavelet basis Ψ rarefaction representations, sparse coefficient θ=Ψ x are obtained；
C, chaos sequence calculation matrix Φ is constructed；
D, sparse coefficient is observed using calculation matrix, obtains compressed data y=Φ θ+n, n represents noise；
E, improved Bayes's wavelet tree structure compresses sensing reconstructing algorithm (BTSWCS-vb) is designed；
F, the sparse coefficient of partial data is solved with restructing algorithm
G, to the sparse coefficient inverse transformation tried to achieve, complete microseism data is obtained
H, make comparisons with other algorithms, analyze result of the test, calculation matrix and restructing algorithm are evaluated, evaluation criterion
It is compression time (Time), Y-PSNR (PSNR) and root-mean-square error (RMSE).
Compared with prior art, the beneficial effects of the present invention are：
The present invention is based on compressive sensing theory, and construction chaos sequence calculation matrix compresses the conversion coefficient of seismic data,
Meets the needs of seismic data Real Time Compression, and algorithm is simple, is easy to hardware realization.The present invention is small with improved Bayes
Ripple tree construction compressed sensing restructing algorithm High precision reconstruction goes out complete initial data, contacted between compression process and restructuring procedure compared with
It is few, the problem of conventional compact mode cannot meet low compression ratio compression and High precision reconstruction at the same time is efficiently solved, and calculate
Method relative to convergence rate before improvement faster, computational efficiency higher.When being compressed according to the present invention to seismic data, it is pressed
Ratio contract between 0.2~0.55, not only greatly alleviates transmission of seismic data speed and storage pressure, but also reduce earthquake
The burden of data transfer and monitoring cost etc..
Brief description of the drawings
Fig. 1 is seismic data compression and reconstructing method flow chart；
Fig. 2 is wavelet coefficient spatial orientation tree set membership schematic diagram；
Fig. 3 is chaos sequence calculation matrix construction flow chart；
Fig. 4 a compare for several chaos sequence calculation matrix performances --- PSNR line charts；
Fig. 4 b compare for several chaos sequence calculation matrix performances --- RMSE line charts；
Fig. 5 is improved Bayes's wavelet tree structure compresses sensing reconstructing algorithm prior model；
Fig. 6 a are BTSWCS-vb algorithms compared with BTSWCS-mcmc performances --- Time line charts；
Fig. 6 b are BTSWCS-vb, BTSWCS-mcmc and CoSaMP algorithm comparison --- PSNR line charts；
Fig. 6 c are BTSWCS-vb, BTSWCS-mcmc and CoSaMP algorithm comparison --- RMSE line charts；
Fig. 7 a are experiment actual seismic datagram used；
After Fig. 7 b is do 0.25 observation with Logistic matrixes, then the seismic data recovered with BTSWCS-vb algorithms.
Embodiment
The present invention is described in detail below in conjunction with the accompanying drawings.
As shown in Figure 1, a kind of seismic data compression and reconstructing method, comprise the following steps：
A, seismic monitoring real data x is inputted；
B, to input data wavelet basis Ψ rarefaction representations, sparse coefficient θ=Ψ x are obtained,
Rarefaction representation process：θ=Ψ x represent that actual seismic data x obtains sparse coefficient θ after wavelet transformation；
C, chaos sequence calculation matrix Φ is constructed；
Matrix construction：The construction of chaos sequence calculation matrix is the key of seismic data compression, measures square according to the present invention
The constitution step of battle array, selects suitable chaos sequence and sets rational initial value, to meet the RIP bars needed for calculation matrix
Part.
D, sparse coefficient being observed using calculation matrix, obtains compressed data y=Φ θ+n, n represents noise,
Compression process：Y=Φ θ+n are represented, on the basis of Successful construct matrix, the measurement square of line number is much larger than with columns
Battle array and sparse coefficient product, so as to reach the effect of dimensionality reduction, obtain compressed value y；
E, improved Bayes's wavelet tree structure compresses sensing reconstructing algorithm (BTSWCS-vb) is designed；
Algorithm improvement：Original Bayes's wavelet tree structure algorithm quality reconstruction is much better than common greedy class algorithm, deficiency
Part is that reconstructing the time used will grow relatively；The improved algorithm of the present invention overcomes this shortcoming, it will be apparent that shortens weight
The structure time.
F, the sparse coefficient of partial data is solved with restructing algorithm
Restructuring procedure：Go out the sparse of complete actual seismic data with improved Bayes's wavelet tree structural remodeling Algorithm for Solving
Coefficient；
G, to the sparse coefficient inverse transformation tried to achieve, complete microseism data is obtained
H, make comparisons with other algorithms, analyze result of the test, calculation matrix and restructing algorithm are evaluated, evaluation criterion
It is compression time (Time), Y-PSNR (PSNR) and root-mean-square error (RMSE).
There are three key technologies in above-mentioned steps：Wavelet transformation, construction calculation matrix, improve restructing algorithm, below to it
Realization principle introduction.
1st, seismic signal theory of wavelet transformation of the present invention：
Since seismic signal is not sparse in time-domain, according to compressive sensing theory, it is necessary to carry out sparse table to it
Show.According to earthquake signal frequency split band property is strong, changes in amplitude is big, the characteristic such as correlation height on two-dimensional space, and wavelet basis and ground
Shake signal has similitude, so carrying out wavelet transformation to seismic signal, obtained wavelet coefficient has good compressibility.
To the discrete wavelet multiresolution analysis of one-dimensional signal (single track microseismic signals), Mallet arthmetic statement formula are：
In formula, Cj,k、Dj,kIt is original signal respectively in scale 2-jIt is lower to decompose obtained low frequency component and high fdrequency component, h (m-
2k), g (m-2k) is the coefficient of low pass and high-pass filter respectively.Corresponding reconstruction formula is as follows：
Decomposition and reconstruction is carried out to two-dimension earthquake signal respectively to be obtained row and column progress one-dimensional wavelet transform, wherein
Line number and row number respectively correspondingly shake time and the Taoist monastic name of section.Present invention selection " db1 " wavelet basis is to seismic signal sparse table
Show.
Wavelet transformation is in addition to increasing the compressibility of seismic signal, such as Fig. 2 wavelet coefficient spatial orientation tree set memberships
Shown, wavelet coefficient also has structures statistics characteristic.Seismic signal generates the small echo of quad-tree structure after wavelet transform
Coefficient, i.e., each wavelet coefficient usually have four " child nodes " as " father node ", and the reconstruct based on Bayes's compressed sensing is calculated
Method just make use of this architectural characteristic of wavelet coefficient.We show the pass in tree on different scale between coefficient with the direction of arrow
System, arrow is to be directed toward child node by father node.The corresponding coefficients of scale S=1 are father node, on out to out S=L (L=3)
Coefficient whistle node.Having in the wavelet coefficient of 1≤S≤L-1 scope classes on scale S+1 has four " sons "；Only low frequency range
All coefficients in domain (S=0) in each group of (2 × 2) matrix in that coefficient of upper left side and high-frequency region (S=3) do not have " youngster
Son ".
To sum up, the coefficient energy after seismic signal wavelet transformation is concentrated very much, and most data level off to 0, can in signal
Tended to collect in together with the wavelet coefficient ignored；On the other hand, father and son's node has certain relation, such as, if one is
Number is negligible on a scale, his child node is generally also negligible.
2nd, the aufbauprinciple of chaos sequence calculation matrix of the present invention：
Compressive sensing theory is that information as much as possible is obtained by few data with Exact recovery original signal, because
The effect of this calculation matrix is most important.Although the reconstruct of the random measurement matrixes such as the Gauss generally used at present, Bernoulli Jacob
Results contrast is good, but in l-G simulation test, the uncertainty of calculation matrix directly has an impact the robustness of experimental result；And
In practical engineering application, the problems such as computation complexity is high, memory space is big, hardware is not easy to realize can be also caused.
Chaos is considered to have the phenomenon of the certainty dynamical system of random sexual behaviour.Since chaos sequence itself is good
Pseudo-randomness feature, and the complexity of chaos sequence calculation matrix is constructed well below random measurement matrix, so its work
Cheng Shixian is significant.As shown in figure 3, present invention construction chaos sequence calculation matrix step is：
Step 1, according to the transformation kernel of related chaotic maps, choosing suitable sequence initial value and systematic parameter makes chaos system
System reaches preferable chaos state to produce chaos sequence { un, sequence length is M × N-1.
Step 2, chaos sequence { u step 1 generatednSequence { a is mapped to by sign functionn}。
Step 3, sequence { a step 2 generatednN long is taken to block to form M × N-dimensional calculation matrix Φ.
The present invention uses several chaos sequences construction calculation matrix as described below：
The first：Logistic (Rochester) mapping transformation core：xn+1=axn(1-xn) (3)
Initial value is set：x0=0.37, a=2；Sign function：
Second：Henon (angstrom agriculture) mapping transformation core：
Initial value is set：x0=y0=0, a=1.4, b=0.314；Sign function：
The third：Tentmap (tent) mapping transformation core：xn+1=a- (1+a) | xn|, (5)
Initial value is set：x0=0.01, a=0.99；Sign function：
4th kind：Kent (Kent) mapping transformation core：
Initial value is set：x0=0.36, a=0.8；Sign function：
The use of sign function from construction process, it can be seen that：Compared to common random measurement matrix, of the invention is mixed
Ignorant calculation matrix be easy for it is hard-wired because the element of matrix only has 0,1 and -1.To evaluate several chaotic measurement matrixes
Performance, seismic data is carried out different degrees of compression with different calculation matrix, then unifies to be reconstructed with CoSaMP algorithms, knot
As shown in fig. 4 a, influence of several chaotic measurement matrixes to reconstruction result PSNR values, Fig. 4 b are to reconstruction result RMSE value to fruit
Influence.As can be seen that Matrix Construction Method and initial value, the selection of parameter by the present invention, comparatively Logistic matrixes
Effect is best.
3rd, improved Bayes's wavelet tree structure compresses sensing reconstructing algorithm (BTSWCS-vb) design principle of the present invention：
To overcome under low signal-to-noise ratio, the problem of greedy algorithm reconstruction property is poor is commonly used, present invention contemplates that Bayes's frame
Compressed sensing restructing algorithm under frame, Bayes's wavelet tree structure compresses perception algorithm (BTSWCS).The algorithm assumes initially that shellfish
Each element obeys nail riveting prior distribution in this prior model sparse coefficient of leaf θπiRepresent
Weight, δ0Be partially shown as 0 coefficient, then with Markov Chain Monte Carlo (Markov Chain Monte Carlo,
MCMC) reasoning is to prior model parameter Posterior estimator, still, is grown using the reconstitution time needed for MCMC reasonings, and convergence rate is slow,
Computational efficiency is low.
Variation Bayes (Variational Bayesian, VB) reasoning extensively should in Bayesian analysis in recent years
With because it can not only provide an accurate posteriority, but also VB reasoning fast convergence rates, computational efficiency is high.Institute of the present invention
Improve the use of BTSWCS algorithms Posterior estimator is exactly VB reasonings, the identical number of iteration compared with MCMC reasonings, reconstruct
Positive effect improves, and reconstitution time significantly reduces.
The present invention is for the ease of using VB reasonings, it is proposed that one with the prior model of nail riveting priori equivalence, do not have in model
There is δ0Components.Prior model is assumed to be Symbolic indication Hadamard products, ω represent to be not zero sparse
Coefficient,Z is instruction parameter, is 0 or 1, zi~Bernoulli (πi)；When sparse coefficient is not 0, component z=
1；Conversely, component z=0.Model can be summarized as：
α0~Gamma (a0,b0) (9a)
αs~Gamma (c0,d0),0≤s≤L (9b)
Subscript (s, i) represents i-th of element on wavelet transform dimension s, and pa (s, i) represents relevant father node.Model
Middle πs,iWavelet tree architectural characteristic is applied, different father node value pa (s, i), just there is different instruction parameter z, Study first π
Also it is just different.When the father node value of coefficient is zero, π takes πs0；When the father node value of coefficient is not zero, π takes πs1.Model
Graphic form is with reference to figure 5.VB reasonings carry out the real Posterior distrbutionp p (θ v) of approximate evaluation using a component cloth q (θ), with lower limit F
To approach the true log likelihood function logp (v θ) of model.Algorithm updates q (θ) by iteration, to make F level off to logp (v
θ), until convergence.
Restructing algorithm performance is improved for evaluation, different compression ratios uniformly are carried out with Tentmap matrixes to real data first
Observation, then reconstructed respectively with three kinds of algorithms of different, experimental result takes the average values of 30 experiments, and reconstruction result is compared
Compared with.What Fig. 6 a compared is BTSWCS-vb algorithms and the reconstruct of BTSWCS-mcmc algorithms so the time, it is easy to is found out：BTSWCS-
Time used in vb algorithms is shorter, and calculating speed is faster；Fig. 6 b are that BTSWCS-vb algorithms, BTSWCS-mcmc algorithms and CoSaMP are calculated
Method reconstruction result PSNR values compare, and Fig. 6 c are that three kinds of algorithm reconstruction result RMSE values compare, and are intuitively found out very much：BTSWCS-vb
Algorithm reconstructs Y-PSNR highest, and reconstructed error is minimum, and quality reconstruction is best.Real data such as Fig. 7 a used in present invention experiment
It is shown, after the observation that compression ratio is 0.25 is made of Logistic matrixes, then with BTSWCS-vb algorithms partial data is recovered, tied
As shown in Figure 7b, reconstruction accuracy is very high for fruit, and error only has 0.0058.In conclusion from time, reconstruction result PSNR used in reconstruct
From the point of view of value and reconstruction result RMSE value, innovatory algorithm BTSWCS-vb advantages are obvious.
Claims (1)
1. a kind of seismic data compression and reconstructing method, it is characterised in that comprise the following steps：
A, seismic monitoring real data x is inputted；
B, to input data wavelet basis Ψ rarefaction representations, sparse coefficient θ=Ψ x are obtained；
C, chaos sequence calculation matrix Φ is constructed；
D, sparse coefficient is observed using calculation matrix, obtains compressed data y=Φ θ+n, n represents noise；
E, improved Bayes's wavelet tree structure compresses sensing reconstructing algorithm (BTSWCS-vb) is designed；
F, the sparse coefficient of partial data is solved with restructing algorithm
G, to the sparse coefficient inverse transformation tried to achieve, complete microseism data is obtained
H, make comparisons with other algorithms, analyze result of the test, calculation matrix and restructing algorithm are evaluated, evaluation criterion is pressure
Contracting time (Time), Y-PSNR (PSNR) and root-mean-square error (RMSE).
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