CN116047504A - Method for improving deconvolution to inhibit ground penetrating radar multiple - Google Patents
Method for improving deconvolution to inhibit ground penetrating radar multiple Download PDFInfo
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- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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Abstract
The invention discloses a method for improving deconvolution to inhibit multiple waves of a ground penetrating radar, which comprises the following steps of 1, reading in ground penetrating radar data, preprocessing the data, and: performing differential processing on radar data; normalizing radar data; filtering radar data; step 2, improving predictive deconvolution, comprising: locating a specific position of the reflected wave signal by a local peak method; fitting a reflected wave hyperbola according to the positioned signal position; controlling the intensity factor of the predictive deconvolution using the estimated intensity parameter; and suppressing multiple waves in the ground penetrating radar data through a prediction deconvolution idea, and outputting the ground penetrating radar data after eliminating the multiple wave interference. According to the improved prediction deconvolution method designed by the invention, hyperbolas are automatically fitted and the position of deconvolution for eliminating the multiple is precisely controlled according to the characteristics of similar shapes of the multiple, so that the interference of the multiple is eliminated and the hyperbolas corresponding to the weak target are kept as far as possible.
Description
Technical Field
The invention belongs to the field of intelligent information processing, and particularly relates to a method for improving deconvolution to inhibit multiple waves of a ground penetrating radar.
Background
Currently, the main methods for suppressing the multiples of ground penetrating radar can be divided into three main categories.
The method is characterized in that related information of the ground penetrating radar emission wave needs to be known in advance, radar data is inverted according to a wave equation or electromagnetic waves are simulated to infer a multiple model, and then the multiple model is adaptively subtracted from original radar data to achieve the purpose of suppressing multiple, and the method is called wave equation-based prediction subtraction (Niu Binhua and the like, wave equation multiple suppression technology progress, 2003, wu Di and the like, data driving type multiple attenuation method research, 2008, xie Songlei, wave equation-based complex structure multiple suppression method research, 2013).
One type of reconstruction of the full waveform of the primary and multiples by iterative inversion is called sparse inversion (VAN GROENESTIJN et al., estimation of primaries by sparse inversion from passive seismic data,2010;F.H.C.YPMA et al, estimating primaries by sparse inversion, a generalized approach,2013; yang Xuming, etc., adaptive sparse inversion multiple suppression method, 2020; bao Peinan, etc., inter-layer multiple suppression method based on iterative inversion, 2021).
The method is also called a filtering method (Zhang Chuncheng and the like, research on clutter suppression and synthetic aperture imaging of shallow stratum ground penetrating radar based on hyperbolic characteristics, 2005; li Jiang and the like, sea clutter suppression method based on Radon transformation under high glancing sea angles, 2007; zhu Hongxiang and the like, and the estimation-prediction deconvolution method of crust structure of a sedimentary basin area eliminates the multi-wave reverberation of a receiving function, 2018; ma Jitao, and comparative analysis of multi-wave suppression algorithm based on three-dimensional parabolic Radon transformation)
The existing method has the defects that:
(1) Because the deconvolution algorithm performance depends on accurate algorithm parameter setting, the actual engineering application has higher requirements on parameter selection;
(2) When the multiple wave overlaps with the effective reflected signal waveform of other target objects, it is very difficult to solve the optimal prediction filter factor, and the interference of the multiple wave cannot be removed well by the current method.
Disclosure of Invention
Aiming at regular hyperbolic reflected signals, the invention improves the prediction deconvolution technology in the limitation of solving the optimal prediction filtering factor, provides a method for restraining the multiple of the ground penetrating radar by improving deconvolution, adopts a fitting hyperbolic method, extracts the reflected signal hyperbola and the multiple hyperbola in a designated area, and precisely positions the multiple by comparing the fitting hyperbola, thereby restraining the delay parameter of deconvolution. Under the condition of interference, the optimal prediction filter factor is accurately and conveniently obtained so as to facilitate the subsequent suppression of multiple waves, extract valuable information of the underground and provide correct knowledge for geological interpretation.
The invention discloses a method for inhibiting ground penetrating radar multiple by improved deconvolution, which comprises the following steps:
step 1, reading in ground penetrating radar data, preprocessing the data, and specifically, the steps include:
(1.1) differential processing radar data;
(1.2) normalizing the radar data;
(1.3) filtering the radar data;
step 2, improved predictive deconvolution, comprising the following specific steps:
(2.1) locating the specific position of the reflected wave signal by a local peaking method;
(2.2) fitting a reflected wave hyperbola according to the positioned signal position;
(2.3) controlling the intensity factor of the predictive deconvolution using the estimated intensity parameter;
and (2.4) suppressing multiple waves in the ground penetrating radar data through a prediction deconvolution idea, and outputting the ground penetrating radar data after eliminating the multiple wave interference.
The method has the beneficial effects that:
(1) The input hyperbola ground penetrating radar data is preprocessed, irrelevant information such as noise interference which possibly exists can be eliminated greatly, hyperbola characteristics are reserved and enhanced, and reliability and stability of fitting a hyperbola are improved.
(2) According to the improved prediction deconvolution method designed by the invention, hyperbolas are automatically fitted and the position of deconvolution for eliminating the multiple is precisely controlled according to the characteristics of similar shapes of the multiple, so that the interference of the multiple is eliminated and the hyperbolas corresponding to the weak target are kept as far as possible.
Drawings
FIG. 1 is a flow chart of an improved predictive deconvolution algorithm.
Detailed Description
The present invention will be further described with reference to examples and drawings, but the present invention is not limited thereto.
Examples
A method for improving deconvolution to suppress ground penetrating radar multiples, comprising the steps of:
step 1, reading in ground penetrating radar data, preprocessing the data, and specifically, the steps include:
(1.1) differential processing radar data;
let i be the number of lanes of the ground penetrating radar data, the parameter t represents the number of time samples per lane, d i And (t) is the mathematical expression of the ith path of ground penetrating radar data, namely the input ground penetrating radar data D (t), and the mathematical expression is shown in the formula (1):
D(t)＝{d 1 (t),...,d i (t),...,d L (t)},t＝1,2,...,T (1)
performing channel difference on the D (t) to reduce irregular fluctuation among data, and eliminating possible noise interference as far as possible so as to enable a reflection curve of the D (t) to be more stable; in the embodiment, each path of data of the ground penetrating radar data is subjected to differential processing by adopting a second-order differential idea of image processing, so as to obtain radar data u after the path is differential i (t) the curve shape of the reflected wave can be highlighted better than the original data.
(1.2) normalizing the radar data;
normalizing as a way of simplifying calculation, converting a dimensioned expression into a dimensionless expression so as to enable the dimensionless expression to be a pure quantity with a fixed standard form; the embodiment adopts a Min-Max standardization method to carry out the ground penetrating radar data u i (t) scaling to [0,1 ]]Between them, get n i And (t) normalized ground penetrating radar data.
(1.3) filtering the radar data;
the data filtering can reduce noise interference, reduce the influence of data quality downslide caused by data loss, and if a certain point is obviously different from the surrounding, the point is infected by noise, and abrupt data points need to be removed;
first, normalized ground penetrating radar data n i (t) adopting a neighborhood average method to obtain radar data m after neighborhood average processing i (t),t＝1,2,...T；
Next, a filter is created for m by estimating the local mean and variance around each data point of the ground penetrating radar data i (t) filtering to obtain the i-th filtered ground penetrating radar data, wherein the formula (2) is as follows:
W(t)＝{w 1 (t),...,w i (t),...w L (T) }, t=1, 2,..t, W (T) is the filtered ground penetrating radar data,
the wiener filter for representing filtering can adapt to the local variance of the image by itself, and when the variance is large, the smoothing process is hardly executed; when the variance is smaller, more smoothing processing is performed;
step 2, improved predictive deconvolution, wherein the algorithm flow chart is shown in fig. 1, and the improved method comprises the following specific steps:
(2.1) locating the specific position of the reflected wave signal by a local peaking method;
after preprocessing the data, the specific position of the reflected wave needs to be positioned so as to restrict the deconvolution to eliminate the position area of the multiple wave, and the reflected wave is mostly parabolic or hyperbolic, so that the peak value is found to be a breakthrough point for positioning the position of the object;
in this embodiment, the exact position of the reflected wave is located by local peak values, and the data w is first recorded for each track i (t) performing first-order differential calculation to obtain g i (t) searching for possible peaksValues, see formula (3):
P i ＝{g i (t)||g i (t)-β|＞3σ 2 } (3)
in the formula (3), beta and sigma 2 G is g i (t) means and variances, and finally, P is obtained i The position of the maximum is used for hyperbolic fitting, see formula (4):
v i ＝{t|g i (t)＝maxP i } (4)
the position l of the reflected wave is obtained through the algorithm processing i Information, where l i Is key information, and the position l is needed to be utilized i To fit and optimize the hyperbolic expression of the reflected wave and minimize the error between the fitted hyperbola and the actual reflected wave, let the set of position coordinates consist of i ＝{(i,v i )|1≤i≤L,1≤v i T is the final calculated value.
(2.2) fitting a reflected wave hyperbola according to the positioned signal position;
determination of reflected wave position by local peak method i Then, to further estimate the shape of the reflected wave, the method needs to be implemented by a method of fitting a hyperbola, and the fitted hyperbola can estimate the position shape of the multiple reflected wave so as to directly subtract the interference of the multiple wave in the original data at the later stage;
let the parameter set of the hyperbola be h= { h 1 ,h 2 ,h 3 ,h 4 }，h 1 Fitting the angle; h is a 2 The method comprises the steps of taking the range average value of the offset distance i of data to be fitted as the range average value; h is a 3 The average value of the offset distance i of the data to be fitted is obtained; h is a 4 For the position w of the peak value of the data to be fitted i Average value of (2);
the ordinate of the data to be fitted is known as v i Assume that the ordinate of the fitting data is y i According to the least squares theorem, hyperbolic fitting can be changed into a constrained optimization problem, see equation (5):
wherein ,
representing the optimized parameter value, and knowing that the fitted hyperbola corresponds to each data position
See (6): />
(2.3) controlling the intensity factor of the predictive deconvolution using the estimated intensity parameter;
assuming that the frame-selected reflected signal region is located from the zeta to epsilon channels, sampling from time
To->
The multiple hyperbola is obtained by scanning D (t), assuming a scanning step of λ, i.e. the first scanning position is from zeta to epsilon, sampling in time +.>
To->
The fitting hyperbola can be obtained in each scanning, and because of the periodicity of the multiple waves, when the multiple waves are successfully obtained, the difference between the shape of the fitting hyperbola and the fitting hyperbola of the reflected signals is small, so that the fitting hyperbola of the reflected signals and the fitting hyperbola of each scanning are compared, and whether the hyperbola obtained in the scanning is the multiple waves can be judged;
let the reflected signal fit hyperbola be:
wherein i is the number of tracks, ">
Fitting hyperbola parameters for the reflected signal, the mth multiple fitting hyperbola is: />
Fitting hyperbolic parameters to the reflected signals;
firstly, calculating the fitting degree of hyperbolas corresponding to the reflected signals and hyperbolas corresponding to the multiple, wherein the fitting degree is shown in a formula (7):
when the fitting degree of the hyperbola corresponding to the reflected signal and the hyperbola corresponding to the multiple meets the following condition, the following formula (8) is adopted, namely the successful positioning of the multiple is judged;
δm＜T (8)
wherein T is a judgment threshold, and when the fitting degree is smaller than the judgment threshold, the successful positioning of the hyperbola corresponding to the multiple wave can be judged;
then, the predicted deconvolution prediction step alpha is extracted and used for the subsequent deconvolution operation, and the calculation is shown in the formula (9):
(2.4) suppressing multiple waves in the ground penetrating radar data through a prediction deconvolution idea, and outputting the ground penetrating radar data after eliminating the multiple wave interference;
the core problem of predictive deconvolution is to design the deconvolution factor s (t), see equation (10):
where α is the deconvolution prediction step size, ρ is the prediction filter length, c (t) = [ c (0), c (1),. The term, c (ρ) ] is the prediction filter factor, which can be found based on the least squares theorem;
in order to avoid instability caused by zero or close to zero amplitude at a certain frequency of the wavelet amplitude spectrum, pre-whitening treatment is needed to be carried out on the wavelet amplitude spectrum in the solving process;
finally, deconvolution factor s (t) and ith trace data d i (t) convolving to obtain the ith data q after removing the multiple interference i (t) formula (11):
through the steps, the target that the multiple wave energy of the strong target is completely inhibited and the primary wave signal of the weak target is clearer is finally realized, the subsequent geological research analysis is more convenient, and research thinking and method measures which are worth referencing are provided for processing related works.
Claims (1)
1. A method for improving deconvolution to suppress ground penetrating radar multiples, comprising the steps of: step 1, reading in ground penetrating radar data, preprocessing the data, and specifically, the steps include:
(1.1) differential processing radar data;
let i be the number of lanes of the ground penetrating radar data, the parameter t represents the number of time samples per lane, d i And (t) is the mathematical expression of the ith path of ground penetrating radar data, namely the input ground penetrating radar data D (t), and the mathematical expression is shown in the formula (1):
D(t)＝{d 1 (t),...,d i (t),...,d L (t)},t＝1,2,...,T (1)
performing channel difference on D (t), performing difference processing on each path of data of the ground penetrating radar data by adopting a second-order difference idea of image processing, and obtaining ground penetrating radar data u after channel difference i (t)；
(1.2) normalizing the radar data;
the Min-Max standardization method is adopted to carry out the ground penetrating radar data u i (t) scaling to [0,1 ]]Between them, get n i (t) normalized ground penetrating radar data;
(1.3) filtering the radar data;
first, normalized ground penetrating radar data n i (t) adopting a neighborhood average method to obtain radar data m after neighborhood average processing i (t),t＝1,2,...T；
Next, a filter is created for m by estimating the local mean and variance around each data point of the ground penetrating radar data i (t) filtering to obtain the i-th filtered ground penetrating radar data, wherein the formula (2) is as follows:
W(t)＝{w 1 (t),...,w i (t),...w L (T) }, t=1, 2,..t, W (T) is the filtered ground penetrating radar data,
the wiener filter for representing filtering can adapt to the local variance of the image by itself, and when the variance is large, the smoothing process is hardly executed; when the variance is smaller, more smoothing processing is performed;
step 2, improved predictive deconvolution, comprising the following specific steps:
(2.1) locating the specific position of the reflected wave signal by a local peaking method;
first for each data w i (t) performing first-order differential calculation to obtain g i (t) searching for possible peaks, see formula (3):
P i ＝{g i (t)||g i (t)-β|＞3σ 2 } (3)
in the formula (3), beta and sigma 2 G respectively i (t) means and variances;
finally, find P i The position of the maximum is used for hyperbolic fitting, see formula (4):
v i ＝{t|g i (t)＝maxP i } (4)
the position l of the reflected wave is obtained through the algorithm processing i Information, where l i Is key information, and the position l is needed to be utilized i To fit and optimize the hyperbolic expression of the reflected wave and minimize the error between the fitted hyperbola and the actual reflected wave, let the set of position coordinates consist of i ＝{(i,v i )|1≤i≤L,1≤v i T is the final calculated value;
(2.2) fitting a reflected wave hyperbola according to the positioned signal position;
let the parameter set of the hyperbola be h= { h 1 ,h 2 ,h 3 ,h 4 }，h 1 Fitting the angle; h is a 2 The method comprises the steps of taking the range average value of the offset distance i of data to be fitted as the range average value; h is a 3 The average value of the offset distance i of the data to be fitted is obtained; h is a 4 For the position w of the peak value of the data to be fitted i Average value of (2);
the ordinate of the data to be fitted is known as v i Assume that the ordinate of the fitting data is y i According to the least squares theorem, hyperbolic fitting can be changed into a constrained optimization problem, see equation (5):
wherein ,
representing the optimized parameter value, and knowing that the fitted hyperbola corresponds to each data position
See (6):
(2.3) controlling the intensity factor of the predictive deconvolution using the estimated intensity parameter;
assuming that the frame-selected reflected signal region is located from the zeta to epsilon channels, sampling from time
To->
The multiple hyperbola is obtained by scanning D (t), assuming a scanning step of λ, i.e. the first scanning position is from zeta to epsilon, sampling in time +.>
To->
The fitting hyperbola can be obtained in each scanning, and because of the periodicity of the multiple waves, when the multiple waves are successfully obtained, the difference between the shape of the fitting hyperbola and the fitting hyperbola of the reflected signals is small, so that the fitting hyperbola of the reflected signals and the fitting hyperbola of each scanning are compared, and whether the hyperbola obtained in the scanning is the multiple waves can be judged;
let the reflected signal fit hyperbola be:
wherein i is the number of tracks, ">
Fitting hyperbola parameters for the reflected signal, the mth multiple fitting hyperbola is: />
Fitting hyperbolic parameters to the reflected signals;
firstly, calculating the fitting degree of hyperbolas corresponding to the reflected signals and hyperbolas corresponding to the multiple, wherein the fitting degree is shown in a formula (7):
when the fitting degree of the hyperbola corresponding to the reflected signal and the hyperbola corresponding to the multiple meets the following condition, the following formula (8) is adopted, namely the successful positioning of the multiple is judged;
δ m ＜T (8)
wherein T is a judgment threshold, and when the fitting degree is smaller than the judgment threshold, the successful positioning of the hyperbola corresponding to the multiple wave can be judged;
then, the predicted deconvolution prediction step α is extracted for the subsequent deconvolution operation, and the calculation is given by formula (9):
(2.4) suppressing multiple waves in the ground penetrating radar data through a prediction deconvolution idea, and outputting the ground penetrating radar data after eliminating the multiple wave interference;
the core problem of predictive deconvolution is to design the deconvolution factor s (t), see equation (10):
where α is the deconvolution prediction step size, ρ is the prediction filter length, c (t) = [ c (0), c (1),. The term, c (ρ) ] is the prediction filter factor, which can be found based on the least squares theorem;
finally, deconvolution factor s (t) and ith trace data d i (t) convolving to obtain the ith data q after removing the multiple interference i (t) formula (11):
and finally, the targets with strong targets and clearer primary wave signals of the weak targets are realized, wherein the wave energy of the strong targets is completely inhibited.
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