CN109212590B - Amplitude-preserving reverse time migration low-frequency noise suppression method and system - Google Patents
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Abstract
The invention provides an amplitude-preserved reverse time migration low-frequency noise suppression method and system, wherein the method comprises the following steps: inputting seismic data; performing conventional reverse time migration; carrying out low-frequency noise suppression by adopting an amplitude-preserving low-frequency filtering algorithm; and obtaining a filtering result. The amplitude-preserving low-frequency filtering algorithm firstly carries out sliding fitting on the noise to estimate the low-frequency noise from the seismic signal, and then subtracts the low-frequency noise from the original signal to obtain a filtering result. The method of the invention can realize amplitude-preserving suppression of low-frequency noise generated by reverse time migration RTM. Compared with the conventional method, the method has the advantages of more accurate calculation result and no residual false image. And the data before and after the compression are subtracted to obtain the compression noise, which shows that the invention realizes the effective compression of the low-frequency noise without losing the amplitude characteristic of the effective signal.
Description
Technical Field
The invention relates to a novel technology for carrying out reverse time migration noise suppression on artificial seismic waves, which can be applied to the field of geological surveying such as petroleum exploration and mineral exploration, in particular to an amplitude-preserved reverse time migration low-frequency noise suppression method and system.
Background
China's oil exploration faces various difficulties such as complex earth surface, complex structure, complex cause and the like. Among them, reverse time migration imaging (RTM) is an effective method for solving these problems. The method directly solves the wave equation, has the characteristics of no propagation angle limitation, capability of imaging the refracted wave and the multiple wave and the like, can still accurately image when the speed is changed violently, but various wave field imaging inevitable false images exist, and strong low-frequency noise interference is easy to generate particularly at the position where the reflection interface exists.
After a 2007 GPU (graphical Processing Unit) is used as a coprocessor of a CPU in the general computing field, the reverse time migration algorithm is improved by dozens of times compared with the CPU, and the method is widely applied to the industry. With the rapid development of GPU high-performance calculation, reverse time migration has been an important task in industrial production in China. However, while reverse time migration techniques are rapidly developing, they still need to face: strong low frequency background noise, non-fidelity amplitude, etc. Amplitude preserving reverse time migration low frequency noise suppression techniques are just a solution to these problems.
For RTM low frequency noise, the corresponding compression method mainly includes the following two categories: (1) avoiding back reflection in the wave field construction; (2) selective imaging is performed in the wavefield construction. The first type of suppression method often employs a smooth velocity field to suppress low-frequency noise in wave field construction, and the denoising effect is not particularly ideal and may bring positional deviation to complex structure imaging. The core of the second class of compaction strategy approaches is selective imaging. The main means mainly comprise two aspects: firstly, selecting waves propagated at medium and small angles according to the propagation direction of the waves to perform imaging in the imaging process; and secondly, filtering processing is carried out after imaging. For the compression method in the imaging process, the direction decomposition cross-correlation imaging condition or the angle attenuation factor cross-correlation imaging condition is mainly used. For the post-imaging pressing method, it mainly includes: one-dimensional filtering, multi-dimensional filtering, angle domain gather superposition of medium and small angles, amplitude compensation based on the least square thought and the like. Generally, the method for suppressing low-frequency noise in the imaging process has a good effect, but the calculation amount is large, and the method for suppressing low-frequency noise after imaging has high calculation efficiency, but is not satisfactory in terms of amplitude.
The conventional algorithm flow is shown in fig. 1, and includes the following steps:
a) inputting seismic data;
b) performing conventional Reverse Time Migration (RTM);
c) low-frequency noise suppression (amplitude is not preserved) is carried out by a Laplace filtering method;
d) and outputting an imaging result.
The Laplace filtering method in the step c) cannot perform subtraction between the original signal and the output signal after low-frequency noise suppression, and because Laplace filtering is equivalent to performing secondary differentiation on the signal, that is, the second derivative estimation is only related to the gradient change speed of the original signal, and has no direct numerical magnitude correlation with the original signal, the amplitude preservation characteristic of the effective signal cannot be ensured. The algorithm of the invention aims to provide a brand-new filtering method to avoid the problems of the Laplace filtering method.
Disclosure of Invention
Aiming at the problem of insufficient amplitude preservation after imaging in the conventional RTM low-frequency noise suppression method, the invention firstly estimates the low-frequency noise by using the amplitude preservation low-frequency filtering technology, and then subtracts the low-frequency noise from the original signal to obtain a filtering result, thereby achieving the purpose of amplitude preservation and suppression of the low-frequency noise.
According to one aspect of the present invention, there is provided an amplitude-preserved reverse time-shifted low frequency noise suppression method, the method comprising:
inputting seismic data;
performing conventional reverse time migration;
carrying out low-frequency noise suppression by adopting an amplitude-preserving low-frequency filtering algorithm;
and obtaining a filtering result.
Furthermore, the amplitude-preserving low-frequency filtering algorithm firstly carries out sliding fitting on the noise to estimate the low-frequency noise from the seismic signal, and then subtracts the low-frequency noise from the original signal to obtain a filtering result.
Further, least squares based polynomial coefficient estimation implements low frequency noise approximation.
Further, in the amplitude-preserving low-frequency filtering algorithm, let x (i), i ═ 1, …, and the signal sequence where N is the reverse time migration imaging result is:
x(i)＝s(i)+n(i),i＝1,…,N (1)
wherein s (i) and n (i) are low-frequency noise and effective signals in sequence;
further, filtering is performed by moving a processing window with a fixed length point by point on the one-dimensional signal, when the length of the processing window is L, for the I-th signal sampling point, L processing windows are included, and the j-th window is:
[x(I-L+j),…,x(I+j-1)],j＝1,…,L (2)
let us assume that in the jth window containing the ith sample point, the L low frequency noise sample points are approximated by an m-th order polynomial fit as follows:
x(I-L+j+l-1)＝p(l,j,I)+e(l,j,I)+n(I-L+j+l-1),l＝1,…,L (3)
where e (l, j, I) is the fitting error and p (l, j, I) is the fitting result of the m-th order polynomial of the signal in the window.
Further, p (l, j, I) is represented as follows:
wherein, ak(j, I), k is 0, …, m is a polynomial coefficient,
is that
Rounding, polynomial order needs to satisfy m<L。
Further, equation (4) is written in the form of a matrix vector:
pj,I＝Caj,I (5)
wherein p isj,I＝[p(l,j,I)；l＝1,…,L]T，aj,I＝[a0(j,I),a1(j,I),…,am(j,I)]TAnd, and:
further, the coefficients of the polynomial (5) are obtained by minimizing the following objective function:
wherein x isj,I＝[x(I-L+j+l-1)；l＝1,…,L]T，
The least squares solution of the polynomial coefficients is as follows:
aj,I＝(CTC)-1CTxj,I, (8)。
further, the fitting error is used for carrying out inverse weighted fusion on the polynomial fitting results of all the processing windows to obtain the low-frequency noise estimation of the I-th sampling point:
according to another aspect of the present invention, there is provided an amplitude preserving reverse time migration low frequency noise suppression system, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
inputting seismic data;
performing conventional reverse time migration;
carrying out low-frequency noise suppression by adopting an amplitude-preserving low-frequency filtering algorithm;
and outputting an imaging result.
Tests by the actual procedure showed that: the method of the invention can realize amplitude-preserving suppression of low-frequency noise generated by reverse time migration RTM. Compared with the conventional method, the method has the advantages of more accurate calculation result and no residual false image. And the data before and after the compression are subtracted to obtain the compression noise, which shows that the invention realizes the effective compression of the low-frequency noise without losing the amplitude characteristic of the effective signal. The method is applied to seismic wave RTM imaging of actual data in the field of oil exploration, can ensure the reliability of signals and improve the success rate of exploration.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
Fig. 1 shows a prior art flow diagram.
Fig. 2 shows a flow chart of the method of the invention.
FIG. 3 illustrates a velocity model in accordance with an embodiment of the present invention.
Fig. 4 shows a diagram of reverse time migration RTM imaging results according to an embodiment of the present invention.
Fig. 5(a) and (b) show the results of calculations of an embodiment of the present invention compared with the results of a conventional method.
Figure 6 shows the noise removed after application of the method of the invention.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The Reverse Time Migration (RTM) algorithm is a reflected wave imaging algorithm with the highest precision and can be applied to the resource fields of oil exploration, gas field development, mineral resource detection and the like. The RTM algorithm inevitably generates low-frequency noise, the conventional noise suppression method cannot meet the requirement of amplitude preservation, and the problem of incomplete low-frequency noise suppression exists, so that imaging artifacts mislead the interpretation work of geologists.
The invention provides an effective amplitude-preserving low-frequency noise filtering method, which comprises the following steps: the sliding fitting of noise ensures that the effective signal is not damaged; and (4) polynomial coefficient estimation based on least square to achieve maximum noise approximation. The method can perform RTM low-frequency noise suppression on the premise of ensuring that effective signals are not damaged, has the advantages of good amplitude preservation, less false images and more thorough noise suppression compared with the conventional Laplace filtering method, and can improve the success rate of exploration.
The disclosure provides an amplitude-preserved reverse time migration low-frequency noise suppression method, which comprises the following steps:
inputting seismic data;
performing conventional reverse time migration;
carrying out low-frequency noise suppression by adopting an amplitude-preserving low-frequency filtering algorithm;
and obtaining a filtering result.
Preferably, the amplitude-preserving low-frequency filtering algorithm firstly performs sliding fitting on the noise to estimate the low-frequency noise from the seismic signal, and then subtracts the low-frequency noise from the original signal to obtain a filtering result.
Preferably, the least squares based polynomial coefficient estimation implements a low frequency noise approximation.
The amplitude-preserving low-frequency filtering algorithm process of the method is as follows.
Let x (i), i ═ 1, …, and N be the signal sequence of the RTM imaging results
x(i)＝s(i)+n(i),i＝1,…,N (1)
Wherein: s (i) and n (i) are low frequency noise and effective signal in turn. The primary task of filtering is to estimate the low frequency noise s (i) from the seismic signals x (i).
Filtering may be performed using a fixed length processing window moving point-by-point on the one-dimensional signal. When the processing window length is L, there are L processing windows containing it for the I-th signal sample point. Note that the jth window is:
[x(I-L+j),…,x(I+j-1)],j＝1,…,L (2)
assuming that the j window contains the I sample point, the L low frequency noise sample points can be approximated by an m-th order polynomial fit as follows:
x(I-L+j+l-1)＝p(l,j,I)+e(l,j,I)+n(I-L+j+l-1),l＝1,…,L (3)
wherein: e (l, j, I) is the fitting error, p (l, j, I) is the m-order polynomial fitting result of the signal in the window, and formula 3 is the polynomial fitting of the noise term of formula 1, which is the estimation of the low-frequency noise in the window, and the effective signal is not changed.
Equation 3 is further expressed as follows:
wherein, ak(j, I), k is 0, …, m is a polynomial coefficient,
is that
And (6) taking the whole. In addition, the polynomial order needs to satisfy m<L。
Equation (4) can be written in the form of a matrix vector:
pj,I＝Caj,I (5)
wherein p isj,I＝[p(l,j,I)；l＝1,…,L]T，aj,I＝[a0(j,I),a1(j,I),…,am(j,I)]TAnd, and:
the coefficients of this polynomial can be obtained by minimizing the following objective function (i.e., fitting error):
wherein x isj,I＝[x(I-L+j+l-1)；l＝1,…,L]T。
The least squares solution of this polynomial coefficient is as follows:
aj,I＝(CTC)-1CTxj,I, (8)
after the polynomial coefficients are obtained, an estimate p (L, j, I) of the low frequency noise s (I-L + j + L-1) in the jth processing window can be obtained. And (3) performing inverse weighted fusion on the fitting results of the polynomials of all the processing windows by using the fitting errors to obtain the low-frequency noise estimation of the I-th sampling point:
according to another aspect of the present invention, there is provided an amplitude preserving reverse time migration low frequency noise suppression system, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
inputting seismic data;
performing conventional reverse time migration;
carrying out low-frequency noise suppression by adopting an amplitude-preserving low-frequency filtering algorithm;
and outputting an imaging result.
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
And adopting wave equation reverse time migration as a basic migration algorithm, and realizing reverse time migration and low-frequency noise suppression according to the ideas of fig. 1 and fig. 2. The method of the invention and the conventional method are respectively adopted for test comparison. The hardware environment of the embodiment is as follows: a processor: intel (R) Xeon (R)2.66 GHz; secondly, memory: 48 GB; ③ the GPU: nvidia GPU K10. The software environment is as follows: operating a system: red Hat Enterprise Linux 4-64Update 5; (ii) a parallel computing environment: MPICH1.2.6 are provided. The number of the computing nodes is 20, and the data transmission between the nodes is realized by adopting a ten-gigabit Ethernet connection mode.
Specifically, a model data is selected as a velocity model shown in fig. 3, an observation system collects the whole grid for the earth surface wide azimuth while receiving the reflected seismic waves, the grid dimension is (901, 901, 501), the three-dimensional collection mode of the grid bin (15m, 15m, 10m), an artificial source is added to the grid point of (30m ) designed for a single shot, the single shot number can be calculated to be 450X 450 ═ 202500 shot data, and the total data volume is 3 TB.
The reverse time migration algorithm is used for migration imaging, and the calculation result is shown in fig. 4, so that a large amount of low-frequency noise exists in the imaging result, and imaging of a real stratum is covered. Respectively applying a conventional Laplace filtering method and the amplitude-preserving low-frequency filtering method for processing. As shown in fig. 5, both the conventional Laplace filtering method and the amplitude-preserving low-frequency filtering method of the present invention can obtain results similar to those of the model (fig. 3), but the Laplace filtering method still has artifact residues (shown by arrows in fig. 5 (b)), but the amplitude-preserving low-frequency filtering method of the present invention well suppresses the remaining artifacts, so that the calculation result is more accurate.
In addition, as shown in fig. 6, the amplitude-preserving low-frequency filtering method of the present invention can perform subtraction with the RTM imaging result (fig. 4), and only the low-frequency component in the removed noise is not damaged by the effective signal, which indicates that the amplitude-preserving characteristic of the amplitude-preserving low-frequency filtering method of the present invention is very good. The amplitude of the conventional Laplace filtering method after low-frequency noise suppression and the original RTM imaging result is not in an order of magnitude, so that the subtraction operation cannot be carried out, and the amplitude of an effective signal cannot be maintained theoretically.
Tests by the actual procedure showed that: the method of the invention can realize amplitude-preserving suppression of low-frequency noise generated by reverse time migration RTM. The calculation results are shown in fig. 5(a) and 6, and compared with the conventional method, the method of the invention has more accurate calculation results and no residual artifacts. And the suppressed noise can be obtained by subtracting the data before and after the suppression, as shown in fig. 3, which shows that the present invention realizes the effective suppression of the low frequency noise without losing the amplitude characteristic of the effective signal. The method is applied to seismic wave RTM imaging of actual data in the field of oil exploration, can ensure the reliability of signals and improve the success rate of exploration.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (3)
1. An amplitude-preserving reverse time migration low frequency noise suppression method, characterized in that the method comprises:
inputting seismic data;
performing conventional reverse time migration;
carrying out low-frequency noise suppression by adopting an amplitude-preserving low-frequency filtering algorithm;
obtaining a filtering result;
the amplitude-preserving low-frequency filtering algorithm firstly carries out sliding fitting on noise to estimate low-frequency noise from seismic data, and then subtracts the low-frequency noise from the original seismic data to obtain a filtering result;
polynomial coefficient estimation based on least square realizes low-frequency noise approximation;
filtering by moving a processing window with a fixed length point by point on a one-dimensional signal, wherein when the length of the processing window is L, L processing windows including the sampling point of the I-th signal are counted as the jth window:
[x(I-L+j),…,x(I+j-1)],j＝1,…,L (2)
in the j window containing the I sampling point, L low-frequency noise sampling points are approximated by an m-order polynomial fitting as follows:
x(I-L+j+l-1)＝p(l,j,I)+e(l,j,I)+n(I-L+j+l-1),l＝1,…,L (3)
wherein e (l, j, I) is the fitting error, and p (l, j, I) is the fitting result of the m-order polynomial of the signal in the window;
p (l, j, I) is represented as follows:
wherein, ak(j, I), k is 0, …, m is a polynomial coefficient,
is that
Rounding, polynomial order needs to satisfy m<L；
Equation (4) is written in the form of a matrix vector:
pj,I＝Caj,I (5)
wherein p isj,I＝[p(l,j,I)；l＝1,…,L]T，aj,I＝[a0(j,I),a1(j,I),…,am(j,I)]TAnd, and:
the coefficients of polynomial (5) are obtained by minimizing the following objective function:
wherein x isj,I＝[x(I-L+j+l-1)；l＝1,…,L]T，
The least squares solution of the polynomial coefficients is as follows:
aj,I＝(CTC)-1CTxj,I, (8)；
and (3) performing inverse weighted fusion on the polynomial fitting results of all the processing windows by using fitting errors to obtain the low-frequency noise estimation of the I-th sampling point:
2. the amplitude-preserving reverse time migration low-frequency noise suppression method according to claim 1, wherein in the amplitude-preserving low-frequency filtering algorithm, let x (i), i-1, …, and N be the signal sequence of the reverse time migration imaging result:
x(i)＝s(i)+n(i),i＝1,…,N (1)
wherein s (i) and n (i) are low frequency noise and effective signal in sequence.
3. An amplitude preserving reverse time migration low frequency noise suppression system, comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
inputting seismic data;
performing conventional reverse time migration;
carrying out low-frequency noise suppression by adopting an amplitude-preserving low-frequency filtering algorithm;
outputting an imaging result;
the amplitude-preserving low-frequency filtering algorithm firstly carries out sliding fitting on noise to estimate low-frequency noise from seismic data, and then subtracts the low-frequency noise from the original seismic data to obtain a filtering result;
polynomial coefficient estimation based on least square realizes low-frequency noise approximation;
filtering by moving a processing window with a fixed length point by point on a one-dimensional signal, wherein when the length of the processing window is L, L processing windows including the sampling point of the I-th signal are counted as the jth window:
[x(I-L+j),…,x(I+j-1)],j＝1,…,L (2)
in the j window containing the I sampling point, L low-frequency noise sampling points are approximated by an m-order polynomial fitting as follows:
x(I-L+j+l-1)＝p(l,j,I)+e(l,j,I)+n(I-L+j+l-1),l＝1,…,L (3)
wherein e (l, j, I) is the fitting error, and p (l, j, I) is the fitting result of the m-order polynomial of the signal in the window;
p (l, j, I) is represented as follows:
wherein, ak(j, I), k is 0, …, m is a polynomial coefficient,
is that
Rounding, polynomial order needs to satisfy m<L；
Equation (4) is written in the form of a matrix vector:
pj,I＝Caj,I (5)
wherein p isj,I＝[p(l,j,I)；l＝1,…,L]T，aj,I＝[a0(j,I),a1(j,I),…,am(j,I)]TAnd, and:
the coefficients of polynomial (5) are obtained by minimizing the following objective function:
wherein x isj,I＝[x(I-L+j+l-1)；l＝1,…,L]T，
The least squares solution of the polynomial coefficients is as follows:
aj,I＝(CTC)-1CTxj,I, (8)；
and (3) performing inverse weighted fusion on the polynomial fitting results of all the processing windows by using fitting errors to obtain the low-frequency noise estimation of the I-th sampling point:
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