CN113534239A - Waveform compression method based on signal separation - Google Patents
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Abstract
The invention provides a waveform compression method based on signal separation, which comprises the following steps:
data preprocessing,
Initializing the signaling operator,
Calculating the inverse signal,
Establishing an independent signal equation,
Optimizing an objective function,
Defining a non-linear function,
Solve the equations and
judging whether convergence occurs according to the result of the solution equation, obtaining the optimal solution if the convergence occurs, and returning to the optimal solution if the convergence does not occur
Calculating the inverse signal,
Establishing an independent signal equation,
Optimizing an objective function,
Defining a non-linear function and
the solution equations loop until convergence. The method abandons some assumed conditions needed by the conventional method, is more practical in algorithm, and is a more advanced method. The method has the advantages of simple process and parameter setting, high operation speed and accurate calculation result. The effect of waveform compression is obvious, and the resolution ratio is effectively improved.
Description
Technical Field
The invention belongs to the field of seismic data processing of oil-gas exploration, and relates to a method for compressing seismic wavelets and improving resolution.
Prior Art
The existing method for improving resolution generally adopts a deconvolution method, which is a technology for compressing seismic wavelets and improving the resolution of seismic data by a certain digital processing method.
For example, CN107678065B discloses a "method and apparatus for enhanced seismic resolution protected well-controlled spatial deconvolution", wherein the method for enhanced seismic resolution protected well-controlled spatial deconvolution includes:
101, obtaining a first reflection coefficient matrix based on a logging curve;
step 102, giving an initial spatial regularization parameter sequence λ i, i is 1, 2.. and N, N is the total number of channels included in the original multi-channel seismic record, and entering step 103;
103, performing spatial deconvolution on the multi-channel seismic records based on the current spatial regularization parameter sequence by regularization along a spatial direction to obtain a second reflection coefficient matrix, and entering step 104;
104, if the obtained error between the first reflection coefficient matrix and the second reflection coefficient matrix meets a preset expectation, entering step 106, otherwise, entering step 105;
step 105, adjusting the current spatial regularization parameter sequence, and then returning to step 103;
and 106, taking the second reflection coefficient matrix obtained currently as a final reflection coefficient matrix, performing convolution on the reflection coefficient sequence corresponding to each channel and the wavelet of the channel, and superposing convolution results of multiple channels to obtain the required post-stack seismic data volume.
According to the invention, by maintaining and constructing the well control space deconvolution, the problem of transverse instability of reflection coefficients or wave impedance caused by a single-channel seismic data regularization method is solved; meanwhile, well information is introduced into an inversion result through an initial model, and constraint and quality control are carried out, so that the purpose of improving the seismic data resolution is achieved.
In practice, seismic deconvolution is essentially a hypothetical process because the subsurface formations are unknown, and the excited seismic wavelets are also unknown, so the seismic wavelets cannot be directly isolated from the seismic record. Conventional deconvolution methods commonly used today often require statistical assumptions such as: the formation inverse signal operator is gaussian white; seismic wavelets are minimum phase, and so on. Although the traditional deconvolution method has good effect in practical application, the assumed condition can not be guaranteed to be always correct, because the actual stratum inverse signal operator is also non-Gaussian white, and seismic wavelets propagating in the underground medium are often mixed-phase.
The object of the invention is to provide a method for producing a high-purity calcium carbonate.
Along with the continuous improvement of oil and gas exploration degree, seismic exploration object is more and more complicated, changes complicated hidden oil and gas reservoir from the structure oil and gas reservoir gradually to make the degree of difficulty of oil and gas field exploration and development constantly increase, requirement to seismic exploration technique is more and more high. In order to find complex formations and blind hydrocarbon reservoirs, seismic processing techniques are required to provide high signal-to-noise ratio, high resolution and high fidelity seismic data, with high resolution research being one of the key points in seismic data processing. The invention provides a waveform compression technology based on signal separation without any hypothesis, which is based on the characteristics of seismic records and solves an inverse signal operator without prior hypothesis.
The invention relates to a method for processing a semiconductor chip.
The signal separation technology is to estimate a source signal and a de-mixing matrix by separating independent signals which are not related to each other only from received mixed information, and the waveform compression method based on the signal separation mainly comprises the steps of establishing an independent signal equation, optimizing an objective function and optimizing an algorithm.
1) Basic principle of the invention
The basic idea of signal separation techniques is to separate the observed mixed signals to obtain mutually independent source signals implicit therein. Signal separation techniques aim to seek a linear representation of non-gaussian distributed data such that the individual components are statistically independent, or as independent as possible. From the viewpoint of signal analysis, the signal separation technique is a very effective independent signal separation technique, and it deals with linear and convolution type mixing of a group of mutually statistically independent source signals, neither knowing how the signals are mixed nor the distribution of the source signals, and finally extracting each independent unit from the mixed signal.
2) The invention specifically comprises the following contents:
a method of waveform compression based on signal separation, comprising:
preprocessing data, initializing a signaling operator, calculating a reverse signal, establishing an independent signal equation, optimizing a target function, defining a nonlinear function, solving the equation, judging whether convergence occurs according to the result of solving the equation, obtaining an optimal solution if the convergence occurs, returning to the step of calculating the reverse signal again, establishing the independent signal equation, optimizing the target function, defining the nonlinear function and solving the equation to circulate until the convergence occurs.
The above scheme further comprises:
the data preprocessing comprises the following steps: loading an observation system, denoising processing, energy amplitude compensation and a source signal s (t).
The initialization inverse signal operator adopts a least square method to initialize an inverse signal,
setting a prediction filtering factor: f (t) ([ f (0), f (1), f (m)) ],
the prediction output is then:
the expected output is: s (t + τ) (τ > 0),
prediction error:
total error energy:
using the least square method to calculate f (i) and make Q maximum, and deducing that the initialized inverse signal operator is o (t) — f (t).
The calculation of the inverse signal is the convolution of the source signal and the inverse signal operator: d (t) s (t) o (t).
The establishing of the independent signal equation comprises the following steps: the mathematical expression of the mixed model probability distribution of the fitting inverse signal operator is:
wherein
Means mean 0 and variance is
Is a gaussian distribution, a probability weight coefficient wjSatisfy the requirement of
The probability density function corresponding thereto is:
the optimization objective function is: the optimization objective function of the signal separation algorithm is constructed by using the negative entropy as the measurement of the non-Gaussian degree of the signal, the negative entropy is the KL divergence between any probability density function and the Gaussian probability density function with the same variance, the larger the value of the negative entropy is, the farther the signal is from the Gaussian distribution,
the mathematical expression for negative entropy is as follows:
wherein KL (. circle.) represents the Kullback-Leibler divergence, p (o) represents the probability density function of the Gaussian mixture model, pG(o) represents a probability density function of a gaussian process.
The pair E of inverse signal operators o (t), t 1, …, m is derived and made to be zero
From this it follows
The defining a non-linear function: defining a memoryless nonlinear function according to an independent signal analysis decomposition algorithm:
then the solution equation of the inverse signal operator is simplified as:
the left side of the equation is the product of the Toeplitz matrix of the seismic record autocorrelation and the inverse signal operator, and the right side of the equation is the cross-correlation of the expected output of the independent signal analysis decomposition with the seismic record.
Solving equation (7) to obtain a new inverse signal operator o (t); and (4) judging whether convergence is achieved by adopting a minimum mean square algorithm, obtaining an optimal solution d (t)(s) (t) o (t) if convergence is achieved, and returning to the step (3) to circulate until convergence is achieved if convergence is not achieved.
The data pre-processed data comprises: and collecting line and shot gather of the observation system, seismic data time length, time sampling interval, sampling point number and channel number of each line.
The invention has the advantages.
The waveform compression method based on signal separation effectively compresses the waveform, has the advantages that other methods do not have, and the specific advantages and the characteristics are shown in the following aspects:
first, the advance of the method technology. The method abandons some assumed conditions needed by the conventional method, is more practical in algorithm, and is a more advanced method.
And secondly, the operation is simple and easy to realize. The method has the advantages of simple process and parameter setting, high operation speed and accurate calculation result.
And thirdly, the compression effect of the waveform is better. The effect of waveform compression is obvious, and the resolution ratio is effectively improved.
Drawings
Fig. 1 is a flow chart of a waveform compression method based on signal separation according to the present invention.
FIG. 2 is a conventional deconvolution processing profile.
Fig. 3 is a cross section of a process applying the present invention.
Fig. 4 is a spectral analysis of a conventional processing profile.
Fig. 5 is a spectral analysis of a processing profile to which the present invention is applied.
Detailed Description
Example 1
Referring to fig. 1, a waveform compression method based on signal separation is implemented as follows:
preprocessing data. The method includes loading an observation system, denoising processing, energy amplitude compensation, and the like (the specific implementation methods are many and are not described herein), and outputting a signal (also referred to as a source signal) s (t).
② initializing the signaling operator. Initializing an inverse signal by adopting a least square method, and setting a prediction filtering factor: (t) ═ f (0), f (1), …, f (m)]Then the prediction output is:
the expected output is: s (t + τ) (τ > 0), prediction error:
total error energy:
using the least squares method to find f (i) and maximize Q, we can derive the initialized inverse signal operator as o (t) ═ f (t).
And calculating the inverse signal. The inverse signal is the convolution of the source signal and the inverse signal operator: d (t) s (t) o (t).
Establishing an independent signal equation. The purpose of the signal separation problem is to obtain an inverse signal operator sequence through separation, the inverse signal operator is neither gaussian nor white, therefore, when the probability distribution of the actual inverse signal operator is fitted, a gaussian mixture model is adopted, the gaussian mixture model has the characteristic of approximating any probability density function, and due to the simplicity of the gaussian function, convenience is provided for the updating algorithm of the model parameters. The mathematical expression of the mixed model probability distribution of the fitting inverse signal operator is:
wherein
Means mean 0 and variance is
Is a gaussian distribution, a probability weight coefficient wjSatisfy the requirement of
The probability density function corresponding thereto is:
optimizing the objective function. The inverse signal operator is statistically non-gaussian, so the negative entropy is selected to construct the optimal objective function of the signal separation algorithm. The negative entropy is a measure of the non-gaussian degree of the signal, and is a KL divergence between an arbitrary probability density function and a gaussian probability density function having the same variance, and a larger value of the negative entropy indicates that the signal is farther from the gaussian distribution.
The mathematical expression for negative entropy is as follows:
wherein KL (. circle.) represents the Kullback-Leibler divergence, p (o) represents the probability density function of the Gaussian mixture model, pG(o) represents a probability density function of a gaussian process.
The pair E of inverse signal operators o (t), t 1, …, m is derived and made to be zero
From this it follows
Sixthly, defining a nonlinear function. Defining a memoryless nonlinear function according to an independent signal analysis decomposition algorithm:
then the solution equation of the inverse signal operator is simplified as:
the left side of the equation is the product of the Toeplitz matrix of the seismic record autocorrelation and the inverse signal operator, and the right side of the equation is the cross-correlation of the expected output of the independent signal analysis decomposition with the seismic record.
Solving the equation. Solving equation (7) to obtain a new inverse signal operator o (t)
And determining whether convergence occurs. And (4) judging whether convergence occurs by adopting a least mean square algorithm, obtaining an optimal solution d (t)(s) (t) o (t) if convergence occurs, and returning to the step (3) to circulate until convergence occurs if convergence does not occur.
Application example 2
A waveform compression method based on signal separation is disclosed, and a specific flow chart is shown in figure 1, and the implementation process is as follows:
step 1, data preprocessing. The method includes loading an observation system, denoising processing, energy amplitude compensation, and the like (the specific implementation methods are many and are not described herein), and outputting a signal (also referred to as a source signal) s (t).
And step 2, initializing a signaling operator.
Setting a prediction filtering factor: f (t) ([ f (0), f (1), f (m)) ],
the prediction output is then:
the expected output is: s (t + τ) (τ > 0),
prediction error:
total error energy:
using the least squares method to find f (i) and maximize Q, we can derive the initialized inverse signal operator as o (t) ═ f (t).
And 3, calculating an inverse signal.
The inverse signal is the convolution of the source signal and the inverse signal operator: d (t) s (t) o (t).
And 4, establishing an independent signal equation.
The mathematical expression of the mixed model probability distribution of the fitting inverse signal operator is:
wherein
Means mean 0 and variance is
Gauss ofDistribution, probability weight coefficient wjSatisfy the requirement of
The probability density function corresponding thereto is:
and 5, optimizing the objective function.
The mathematical expression for negative entropy is as follows:
wherein KL (. circle.) represents the Kullback-Leibler divergence, p (o) represents the probability density function of the Gaussian mixture model, pG(o) represents a probability density function of a gaussian process.
The pair E of inverse signal operators o (t), t 1, …, m is derived and made to be zero
From this it follows
And 6, defining a nonlinear function.
Defining a memoryless nonlinear function according to an independent signal analysis decomposition algorithm:
then the solution equation of the inverse signal operator is simplified as:
the left side of the equation is the product of the Toeplitz matrix of the seismic record autocorrelation and the inverse signal operator, and the right side of the equation is the cross-correlation of the expected output of the independent signal analysis decomposition with the seismic record.
And 7, solving the equation.
Solving equation (7) to obtain a new inverse signal operator o (t)
And 8, judging whether convergence occurs or not.
And (4) judging whether convergence occurs by adopting a least mean square algorithm, obtaining an optimal solution d (t)(s) (t) o (t) if convergence occurs, and returning to the step (3) to circulate until convergence occurs if convergence does not occur.
In this embodiment, the XX oil field NZ area three-dimensional seismic data is used as a target area, and the method is applied to process the data so as to verify the effect of the method.
The actual data is acquired by adopting a 32-line 10-cannon observation system, the time length of the seismic data is 7000ms, the time sampling interval is 1ms, the number of sampling points is 3500, and the number of each line is 324. The data is processed by the method.
1) Firstly, step 1 is carried out, data loaded with an observation system are preprocessed, noise interference is removed, and energy compensation is carried out.
2) Then, according to step 2, an initialized inverse signal operator is obtained for subsequent application.
3) And 3, calculating the inverse signal by convolution of the original signal and the inverse signal operator.
4) According to step 4, an independent signal equation is established according to the characteristics of the reflected signal.
5) According to step 5, an optimization objective function is established.
6) According to step 6, a memoryless nonlinear function is defined.
7) And (6) solving the equation in the step (6) to obtain a new inverse signal operator, and completing one iteration operation.
8) And judging whether convergence occurs according to a least mean square algorithm, and if so, obtaining a final inverse signal operator.
And (3) performing convolution operation on the final inverse signal operator and the data in the step (1) to obtain the section of the figure 3. Fig. 2 is a cross section of a conventional deconvolution process, and from a comparison of fig. 2 and fig. 3, the resolution of fig. 3 is significantly improved, the interlayer information is richer, and the waveform is better compressed. Fig. 4 and fig. 5 are the corresponding spectrum analyses of fig. 2 and fig. 3, respectively, from the spectrum analysis, the effective bandwidth of the conventional deconvolution is about 1-47HZ, and the effective bandwidth of the cross section processed by the method is about 1-75HZ, and the high frequency signal is significantly increased.
Claims (10)
1. A method for compressing a waveform based on signal separation, comprising:
preprocessing data, initializing a signaling operator, calculating a reverse signal, establishing an independent signal equation, optimizing a target function, defining a nonlinear function and solving the equation.
2. The signal separation-based waveform compression method according to claim 1, further comprising: judging whether convergence is achieved according to the result of equation solution, obtaining the optimal solution if the convergence is achieved, otherwise returning to the step three, calculating a reverse signal, the step four, establishing an independent signal equation, the step five, optimizing a target function, the step sixthly, defining a nonlinear function and the step seven, and solving the equation to circulate until the convergence is achieved.
3. The signal separation-based waveform compression method according to claim 2, wherein the data preprocessing includes: loading an observation system, denoising processing, energy amplitude compensation and a source signal s (t).
4. The signal separation-based waveform compression method according to claim 3, wherein the initialization inverse signal operator initializes the inverse signal using a least squares method,
setting a prediction filtering factor: f (t) ([ f (0), f (1), f (m)) ],
the prediction output is then:
the expected output is: s (t + τ) (τ > 0),
prediction error:
total error energy:
using the least square method to calculate f (i) and make Q maximum, and deducing that the initialized inverse signal operator is o (t) — f (t).
5. The method of claim 4, wherein the computing of the inverse signal is a convolution of the source signal and an inverse signal operator: d (t) s (t) o (t).
6. The method of claim 5, wherein the establishing of the independent signal equation comprises: the mathematical expression of the mixed model probability distribution of the fitting inverse signal operator is:
wherein
Means mean 0 and variance is
Is a gaussian distribution, a probability weight coefficient wjSatisfy the requirement of
The probability density function corresponding thereto is:
7. the signal separation-based waveform compression method according to claim 6, wherein the optimization objective function: the optimization objective function of the signal separation algorithm is constructed by using the negative entropy as the measurement of the non-Gaussian degree of the signal, the negative entropy is the KL divergence between any probability density function and the Gaussian probability density function with the same variance, the larger the value of the negative entropy is, the farther the signal is from the Gaussian distribution,
the mathematical expression for negative entropy is as follows:
wherein KL (. circle.) represents the Kullback-Leibler divergence, p (o) represents the probability density function of the Gaussian mixture model, pG(o) represents a probability density function of a gaussian process.
The pair E of inverse signal operators o (t), t 1, …, m is derived and made to be zero
From this it follows
8. The signal separation-based waveform compression method of claim 7, wherein the defining a non-linear function: defining a memoryless nonlinear function according to an independent signal analysis decomposition algorithm:
then the solution equation of the inverse signal operator is simplified as:
the left side of the equation is the product of the Toeplitz matrix of the seismic record autocorrelation and the inverse signal operator, and the right side of the equation is the cross-correlation of the expected output of the independent signal analysis decomposition with the seismic record.
9. The method of claim 8, wherein solving equation (7) results in a new inverse signal operator o (t); and (4) judging whether convergence is achieved by adopting a minimum mean square algorithm, obtaining an optimal solution d (t)(s) (t) o (t) if convergence is achieved, and returning to the step (3) to circulate until convergence is achieved if convergence is not achieved.
10. The signal separation-based waveform compression method according to any one of claims 1 to 9, wherein the data preprocessing data includes: and collecting line and shot gather of the observation system, seismic data time length, time sampling interval, sampling point number and channel number of each line.
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