CN105929453B - State estimation method for infinite distribution time lag neural network system with channel fading - Google Patents
State estimation method for infinite distribution time lag neural network system with channel fading Download PDFInfo
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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
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
- G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
- G01V1/48—Processing data
- G01V1/50—Analysing data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6169—Data from specific type of measurement using well-logging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/66—Subsurface modeling
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/70—Other details related to processing
- G01V2210/74—Visualisation of seismic data
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Abstract
The invention provides a state estimation method for an infinite distribution time lag neural network system with channel fading. The method gives consideration to impact on the state estimation performance from random uncertainty, channel fading and infinite distribution time lag, and constructs a Liapunov function to completely use the time lag effective information. Compared with a conventional state estimation method for a neural network dynamic system, the method can gives full consideration to the random uncertainty, channel fading and infinite distribution time lag, depends on a linear matrix inequality solution, achieves an effect of inhibiting noise interference, and further improves the precision of stratum imaging.
Description
Technical field
The invention belongs to geophysics field, is related to a kind of Geophysical Data Processing method, and in particular to one kind has
The method for estimating state of the infinite normed nerve network system of channel fading, the method is suitable for petroleum exploration and development
The seismic velocity model for improving imaging precision and carrying out is set up.
Background technology
Geophysical prospecting for oil is the difference according to subterranean strata physical property, by physical quantity, quality structure over the ground
Make or nature of ground is studied, with search for oil and the geophysical exploration of natural gas.In oil exploration, for by table
Tu ﹑ deserts and sea water cover the area without the direct exposure in rock stratum, rely primarily on geophysical prospecting for oil method and understand indirectly
Geological structure and nature of ground, look for oil and gas is hidden.At present, geophysical prospecting for oil has become one kind that the area of coverage explores for oil
Indispensable means.Currently, complex area low signal-noise ratio data is processed has become Study on processing method and process application technology
The most popular problem of research.Through studying tackling key problem for many years, in fields such as static correction, prestack denoising, velocity modeling, migrations
Existing larger progress, the anisotropy research of speed also begins to be subject to people's attention, but low signal-noise ratio data is processed still
There is the problem that many requires study.
With the development and the continuous improvement of degree of prospecting of geophysical exploration technology, geological condition simply, is easily found
Simple structure trap it is fewer and feweri, what is faced is essentially all the complex reservoir of complex area, i.e. complex area.It is complicated
Area includes complicated earth surface area such as desert, hills, marsh, forest belt etc., and complex reservoir includes complicated structure, stratum, rock
Property and compound enclosure of oil gas reservoir.The relative oil gas geophysical techniques based on seismic prospecting of these complicated exploration crews are carried
New challenge is gone out.Especially in terms of complicated low SNR data process, many insoluble Pinch technologies are still suffered from.Its
One：Affected by surface geology complicated condition, complex area surface relief often change acutely, earth's surface speed it is complicated, earth's surface is non-
Matter causes that the signal to noise ratio of original data is low, serious interference, and face ripple, refracted wave especially scatter noise comparative development, give
The signal to noise ratio of raising data brings larger difficulty；It two：Due to subsurface seismic complex geologic conditions, complex area data construction is made
It is extremely complex, fault development, general to have many set fracture systems especially in complicated fault block region, tomography is i.e. more and little；It is disconnected
Big away from change, from tens meters to hundreds of rice, in addition complex area often igneous rock comparative development, widely distributed, due to fire
The speed of diagenesis is high, there is larger natural impedance difference and country rock between, and its result igneous rock reflection is strong, shields underlying strata
Reflected energy so that the effective reflection target zone energy under igneous rock is weak, it is impossible to imaging very well.In addition, igneous rock generation is more
Subwave produces very strong interference effect to effectively reflection, and the presence of these problems makes original just more complicated wave field become more
Complicate, to the accurately image of construction difficulty is brought.
In imaging control research, state estimation is a primary study problem in recurrent neural network dynamic analysis,
It can be used to solve the signal estimation problems of the aspect such as pattern recognition, dynamic optimization in reality.Current existing state estimation side
Method can not handle together uncertainty, channel fading and the infinite normed of random generation, so as to cause state estimation performance
Decline, but the problem cannot be solved all the time.
The content of the invention
In order to solve the problems, such as techniques as described above, the present invention proposes a kind of Geophysical Data Processing method, specifically relates to
And a kind of method for estimating state of the infinite normed nerve network system with channel fading.Which overcome existing estimation side
Method can not handle together uncertainty, channel fading and the infinite normed of random generation, and then make estimation hydraulic performance decline
The technical problem of defect.
According to technical scheme, there is provided a kind of infinite normed nerve network system with channel fading
Method for estimating state, the method is comprised the following steps：
(1) in the wild in exploration target area in real well with manual method earthquake-wave-exciting, using detection collecting device
Obtain geological data, by field acquisition to geological data carry out pretreatment；
(2) based on pretreated geological data, set up and consider the infinite of the random uncertainty for occurring and channel fading
The dynamic model of neural networks with distributed time delays system；
(3) uncertainty to the random generation of consideration and the dynamic of the infinite normed nerve network system of channel fading
Model carries out state estimation；
(4) according to step (3) to the infinite normed nerve net with the random uncertain and channel fading for occurring
The state estimation of the dynamic model of network system, obtains state estimation error；
(5) the state estimation error obtained according to step (4), obtains state estimation augmented system；
(6) utilization state estimates augmented system, according to liapunov's theorem of stability, obtains state estimator gain square
Battle array；
(7) the state estimation formula for bringing the state estimator gain matrix that step (6) is obtained in step (3) into, completes
State estimation to considering the infinite normed nerve network system of the uncertain and channel fading of random generation；
(8) using the state estimation for obtaining, the imaging of the seismic data of whole target area is carried out.
A kind of Geophysical Data Processing method proposed by the present invention, when it is specially infinite distribution with channel fading
The method for estimating state of stagnant nerve network system, considers wherein the uncertainty of generation, channel fading and infinite point at random
Impact of the cloth time lag to state estimation performance, constructs the effective information that Liapunov function completely make use of time lag, compared to
The method for estimating state of existing neural network dynamic system, the method for estimating state of the present invention can in the lump consider random generation
Uncertainty, channel fading and infinite normed, drawn the method for estimating state for relying on LMI solution, it is real
The effect of existing noise rejection interference, and the method for estimating state is solved and realization is easy, further increases to formation imaging
Precision.
Description of the drawings
Fig. 1 is according to the core procedure schematic flow sheet of the inventive method；
Fig. 2 is nerve network system desired output signal y (k) and the measurement output signal with channel fadingContrast
Figure, in figure dotted line be nerve network system desired output signal y (k), the measurement that solid line is an actually-received for nerve network system
Output signal
Fig. 3 is the estimation difference curve of z (k)Dotted line is z in figure1The estimation difference curve of (k)Solid line is z2
The estimation difference curve of (k)Dotted line is z3The estimation difference curve of (k)
Fig. 4 is perfect condition curve z1(k) and its estimation curveComparison diagram, dotted line represents perfect condition curve in figure
z1K (), solid line represents its estimation curve
Fig. 5 is perfect condition curve z2(k) and its estimation curveComparison diagram, dotted line represents perfect condition curve in figure
z2K (), solid line represents its estimation curve
Fig. 6 is perfect condition curve z3(k) and its estimation curveComparison diagram, dotted line represents perfect condition curve in figure
z3K (), solid line represents its estimation curve
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment a part of embodiment only of the invention, rather than the embodiment of whole.Base
Embodiment in the present invention, those of ordinary skill in the art obtained under the premise of creative work is not made it is all its
His embodiment, belongs to the scope of protection of the invention.
Seismic data processing, refers to and with digital computer the earthquake information of field acquisition is processed and analyzed,
To obtain high-quality, reliable seismic profile, for next step data interpretation provide intuitively, reliable foundation and relevant ground
Matter information.In order to further improve the demand of low signal-to-noise ratio complex structural area seismic prospecting, before without good data base
Put, accurate description underground structure degree sets up accurate stratigraphic structure description, preferably improves the imaging precision of seismic data,
Synthetic geology research for next step provides the key that more preferably full and accurate seismic data is seism processing.
Symbol description
Herein, MTThe transposition of representing matrix M.RnRepresent Euclidean n-space, Rn×mRepresent all n × m ranks reality squares
The set of battle array.I and 0 represents respectively unit matrix, null matrix.Matrix P>0 represents that P is real symmetric tridiagonal matrices, E { x } and E x |
Y } mathematic expectaion of stochastic variable x under the conditions of the mathematic expectaion and y of stochastic variable x is represented respectively.The Europe of | | x | | representation vector x
Norm is obtained in several.diag{A1,A2,…,AnRepresent that diagonal blocks are matrix As1,A2,…,AnBlock diagonal matrix, symbol * is symmetrical
The omission of symmetrical item is represented in block matrix.If M is a symmetrical matrix, λmax(M) eigenvalue of maximum of M is represented.Symbol
Represent Kronecker multiplication.If somewhere does not have clear and definite specified matrix dimension in text, assume that its dimension is adapted to the algebraically of matrix
Computing.
The first specific embodiment that the foundation present invention is provided, as shown in Figure 1, there is provided a kind of Geophysical Data Processing
Method, it is specially a kind of method for estimating state of the infinite normed nerve network system with channel fading, the method
Comprise the following steps：
(1) in the wild in exploration target area in real well with manual method earthquake-wave-exciting, using detection collecting device
Obtain geological data, by field acquisition to geological data carry out pretreatment；
(2) based on pretreated geological data, set up and consider the infinite of the random uncertainty for occurring and channel fading
The dynamic model of neural networks with distributed time delays system；
(3) uncertainty to the random generation of consideration and the dynamic of the infinite normed nerve network system of channel fading
Model carries out state estimation；
(4) according to step (3) to the infinite normed nerve net with the random uncertain and channel fading for occurring
The state estimation of the dynamic model of network system, obtains state estimation error；
(5) the state estimation error obtained according to step (4), obtains state estimation augmented system；
(6) utilization state estimates augmented system, according to liapunov's theorem of stability, obtains state estimator gain square
Battle array；
(7) the state estimation formula for bringing the state estimator gain matrix that step (6) is obtained in step (3) into, completes
State estimation to considering the infinite normed nerve network system of the uncertain and channel fading of random generation；
(8) using the state estimation for obtaining, the imaging of the seismic data of whole target area is carried out.
Wherein, the particular content of step (2) is：Set up based on pretreated geological data and consider the not true of random generation
The dynamic model of the infinite normed nerve network system of qualitative and channel fading；
Set up the dynamic of the infinite normed nerve network system for considering the random uncertainty for occurring and channel fading
Model, its state space form is：
In formula, x (k) is the state vector of k moment neural network dynamic models, and x (k+1) is dynamic for k+1 moment neutral net
The state vector of states model；τ represents the time lag of neural network dynamic system, τ=1,2,3 ..., ∞；X (k- τ) is the k- τ moment
Neural network dynamic model state vector；F (x (k)) is nonlinear activation function that initial condition is zero；When ω (k) is k
Quarter belongs to l2[0, ∞) interference signal；l2[0, ∞) be [0, ∞) on square can and Vecter Function Space；Y (k) is nerve net
The preferable output at network system k moment, z (k) is the linear combination of nerve network system k moment state vectors；A,B,C,D,E,G,H
It is the sytem matrix of known appropriate dimension；The convergence coefficient of distributed delayαiK () is obedience
The stochastic variable of Bernoulli Jacob's distribution, wherein i=1,2,3；Real-valued matrix Δ A, Δ B, Δ C represent uncertain bounded parameters matrix,
Meet [Δ A Δ B Δ C]=MF (k) [N1 N2 N3], wherein M, N1,N2,N3For known normal matrix, F (k) is unknown matrix, full
Sufficient FTK () F (k)≤I, I are unit matrix；
The particular content of step (3) is：Infinite normed to considering the uncertain and channel fading of random generation
The dynamic model of nerve network system carries out state estimation；
State estimator formula：
In formulaIt is the estimated value at the k moment to state vector x (k),It it is the k+1 moment to state vector x (k+
1) estimated value,Actual measurement for the nerve network system k moment is exported,For z (k) the k moment estimated value, Af,
Bf,Cf,DfFor state estimator gain matrix to be asked.
The particular content of step (4) is：According to step (3) to the nothing with the random uncertain and channel fading for occurring
The state estimation of the dynamic model of poor neural networks with distributed time delays system, obtains state estimation error：
Formula (1) is deducted into formula (2), state estimation error equation is obtained：
In formula,For known positive scalar,For channel coefficients,For channel system
Several expectations, orderV (k) is internal system interference；For the state estimation at k moment
Error, e (k+1) is the state estimation error at k+1 moment；For the estimation difference of k moment z (k)；
For αiThe expectation of (k), order
The particular content of step (5) is：According to the state estimation error that step (4) is obtained, state estimation augmentation system is obtained
System；
In above formula, η (k)=[xT(k) eT(k)]T,ξ(k)
=[ωT(k) vT(k)]T, xT(k) for state vector x (k) transposition, eT(k) for state estimation error e (k) transposition, vT(k)
For the transposition that internal system disturbs v (k), wTK () disturbs the transposition of w (k), f for its exteriorT(x (k)) is non-linear excitation letter
The transposition of number f (x (k)),For nonlinear activation functionTransposition.The form of matrix is in formula (4)：
The particular content of step (6) is：Utilization state estimates augmented system, according to liapunov's theorem of stability, obtains
To state estimator gain matrix Af,Bf,Cf,Df。
By formula：
Obtain matrixAnd K, by formula
Solve state estimation gain matrix Af,Bf,Cf,Df；Matrix concrete form in formula (5) and formula (6)：
L=diag { l1,l2,…,ln},
Diag { } represent diagonal matrix, I be unit matrix, symbolKronecker multiplication is represented, γ is known
Performance indications, ε, λ is unknown normal number；It is unknown symmetric positive definite matrix, K,For
Matrix to be asked.
The particular content of step (7) is：The state estimator gain matrix A that step (6) is obtainedf,Bf,Cf,DfBring step into
Suddenly the state estimation formula in (3), completes refreshing to the infinite normed of the uncertain and channel fading of the random generation of consideration
The state estimation of Jing network systems.
Further the present invention provides second specific embodiment, present embodiment be to the first specific embodiment to
A kind of state estimation with the random uncertainty for occurring and the infinite normed nerve network system of channel fading for going out
Method is expanded on further, and the liapunov's theorem of stability described in step (6) is：
V(k+1)-V(k)<0
Wherein：
V (k)=V1(k)+V2(k)+V3(k) (7)
V1(k)=ηT(k)Pη(k),
In formula, V (k) for the k moment Liapunov function, V (k+1) for the k+1 moment Liapunov function, ηT
(k) for η (k) transposition, ηT(l) for η (l) transposition, ηTI () is the transposition of η (i).
Emulated using the method for the invention：
Systematic parameter：
A=diag { 0.78,0.86, -0.25 }, B=diag { 0.05,0.01, -0.02 }, L=diag 0.2,0.5,
0.3 }, G=-0.1,
D=[0.5 0.3-0.7], N1=[- 0.1 0.1 0.2], N2=[0.1-0.2-0.1],
N3=[- 0.2-0.1 0.1], γ=1.1.
Additionally, ψτ=2-3-τ,L=2, f1(x1(k))=- tanh (0.4x1(k))f2
(x2(k))=0.2tanh (x2(k)),f3(x3(k))=tanh (0.6x3(k)).
State estimation gain is solved：
Formula (5) and formula (6) are solved, and obtain state estimator gain matrix Af,Bf,Cf,DfFor following form
State estimator effect：
Fig. 2 is nerve network system desired output signal y (k) and the actual measurement output signal with channel fadingFig. 3 is the estimation difference curve of z (k)Fig. 4 is perfect condition curve z1(k) and its estimation curveFig. 5 is
Perfect condition curve z2(k) and its estimation curveFig. 6 is perfect condition curve z3(k) and its estimation curve
From Fig. 2 to Fig. 6, for the infinite normed nerve with the random uncertainty for occurring and channel fading
Network system, the state estimator design method invented can effectively estimate dbjective state.
The present invention relates to a kind of uncertainty of random generation, channel fading and infinite normed method for estimating state.
Need it is further noted that step (2)-(7) in the inventive method give the method for implementing the step, this area
Technical staff can also further improve the step method according to prior art in details or realization respectively according to understanding.
It is particularly evident to be, using the state of the infinite normed nerve network system with channel fading of the present invention
Method of estimation, is considering that target area data signal to noise ratio is very low, without obvious reflected energy group on normal-moveout spectrum, geological personnel pair
In the case that such region does not have clear and definite geological knowledge, Data of State Estimation is made full use of accurately to describe earth formation
Advantage, carry out data modeling, adoption status estimates that model is merged with the model of other modeling methods according to geological structure
Optimization processing, more accurately determines underground structure model, solves treatment people and builds model in low signal-to-noise ratio complex structural area
Probabilistic puzzlement, improves the imaging precision of the data of low signal-to-noise ratio complex structural area, provides for oil fine granularing scalability
High-quality data foundation.
As described above, method proposed by the present invention has clearly been describe in detail.Although the preferred embodiments of the present invention are detailed
The present invention is carefully described and explains, but those skilled in the art is appreciated that fixed without departing substantially from claims
In the case of the spirit and scope of the present invention of justice, various modifications can be made in form and details.
Claims (3)
1. a kind of method for estimating state of the infinite normed nerve network system with channel fading, the method includes following
Step：
(1) in the wild in exploration target area in real well with manual method earthquake-wave-exciting, obtained using detection collecting device
Geological data, by field acquisition to geological data carry out pretreatment；
(2) based on pretreated geological data, the infinite distribution for considering the random uncertainty for occurring and channel fading is set up
The dynamic model of Delayed Neural Networks system；
(3) uncertainty to the random generation of consideration and the dynamic model of the infinite normed nerve network system of channel fading
Carry out state estimation；
(4) according to step (3) to the infinite normed neutral net system with the random uncertain and channel fading for occurring
The state estimation of the dynamic model of system, obtains state estimation error；
(5) the state estimation error obtained according to step (4), obtains state estimation augmented system；
(6) utilization state estimates augmented system, according to liapunov's theorem of stability, obtains state estimator gain matrix；
(7) the state estimation formula for bringing the state estimator gain matrix that step (6) is obtained in step (3) into, completes to examining
Consider the state estimation of the infinite normed nerve network system of the random uncertainty for occurring and channel fading；
(8) using the state estimation for obtaining, the imaging of the seismic data of whole target area is carried out.
2. according to the state estimation side of the infinite normed nerve network system with channel fading described in claim 1
Method, it is characterised in that the particular content of step (3) is：Infinite point to considering the uncertain and channel fading of random generation
The dynamic model of cloth Delayed Neural Networks system carries out state estimation；
State estimator formula：
X (k) is the estimated value at the k moment to state vector x (k) in formula, and x (k+1) is the k+1 moment to state vector x (k+1)
Estimated value, y (k) is exported for the actual measurement at nerve network system k moment,For z (k) the k moment estimated value, Af,Bf,
Cf,DfFor state estimator gain matrix to be asked.
3. according to the state estimation side of the infinite normed nerve network system with channel fading described in claim 1
Method, it is characterised in that the particular content of step (7) is：The state estimator gain matrix A that step (6) is obtainedf,Bf,Cf,Df
The state estimation formula brought in step (3), completes the infinite distribution of the uncertain and channel fading to considering random generation
The state estimation of Delayed Neural Networks system.
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