CN113687415B - A Gaussian beam migration imaging method in the space-time domain of acoustic media based on dominant frequency approximation - Google Patents
A Gaussian beam migration imaging method in the space-time domain of acoustic media based on dominant frequency approximation Download PDFInfo
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Abstract
本说明书实施例公开了一种基于主频近似的声波介质时空域高斯束偏移成像方法。输入速度参数场和观测数据；确定所述观察数据的主频；根据所述速度参数场，通过对主频近似确定正向波场和时间反传波场的虚部；根据所述正向波场和时间反传波场的虚部进行互相关生成成像结果。本发明既能保持与传统时空域高斯束偏移方法相比拟的成像精度和分辨率，又大大提高了时空域高斯束偏移方法的实现效率，为复杂构造区域的地震数据处理提供了高精度成像保障，且具有更少的时间成本消耗，提高了后续解释工作的质量与效率率。
The embodiment of this specification discloses a Gaussian beam migration imaging method in the space-time domain of acoustic wave media based on dominant frequency approximation. Input the velocity parameter field and observation data; determine the main frequency of the observation data; according to the velocity parameter field, determine the imaginary part of the forward wave field and the time backpropagation wave field by approximating the main frequency; according to the forward wave The imaginary parts of the field and time backpropagated wave fields are cross-correlated to generate imaging results. The present invention can not only maintain imaging accuracy and resolution comparable to the traditional spatiotemporal domain Gaussian beam migration method, but also greatly improve the implementation efficiency of the spatiotemporal domain Gaussian beam migration method, providing high precision for seismic data processing in complex structural areas. Imaging is guaranteed and consumes less time, improving the quality and efficiency of subsequent interpretation work.
Description
Technical Field
The specification relates to the field of exploration geophysics, in particular to a main frequency approximation-based acoustic wave medium time-space domain Gaussian beam offset imaging method.
Background
Traditional frequency domain gaussian beam shifting methods may produce weak illumination and strong artifacts in complex deep structures due to inaccurate paraxial ray tracing of the backward wavefield. For the traditional time-space domain Gaussian beam offset method, the method has better imaging precision, but has higher calculation cost. Therefore, in order to obtain high-precision imaging results, it is necessary to improve the efficiency of the gaussian beam shift imaging method.
Based on this, there is a need for a more accurate and stable imaging method in deep structures.
Disclosure of Invention
The invention aims to provide an accurate and stable imaging method in a deep layer structure.
In order to solve the technical problems, the invention adopts the following technical scheme:
input speed parameter field v and observation data P U (x r T), wherein x r Representing the space coordinates of the receiving points, t being the time of propagation of the seismic waves;
determining the dominant frequency omega of the observed data m ；
From the velocity parameter field v, a forward wave field W is determined by approximation to the dominant frequency (F) (x 0 ,t；x s ) And time-counter-propagating wave field W (R) (x 0 ,t 0 ) Wherein x is 0 Representing the spatial coordinates of the imaging point, x s Representing the space coordinates of the shot point, t is time, t 0 Is the initial value of wave field propagation;
and performing cross-correlation according to the imaginary parts of the forward wave field and the time anti-transmission wave field to generate an imaging result.
The above-mentioned at least one technical scheme that this description embodiment adopted can reach following beneficial effect:
by inputting a speed parameter field and observation data; determining a dominant frequency of the observed data; determining the imaginary parts of the forward wave field and the time inverse wave field by approximating the main frequency according to the speed parameter field; and performing cross-correlation according to the imaginary parts of the forward wave field and the time anti-transmission wave field to generate an imaging result. Compared with the prior art, the method not only can maintain imaging precision and resolution which are compared with the traditional time-space domain Gaussian beam deviation method, but also can greatly improve the realization efficiency of the time-space domain Gaussian beam deviation method. The method develops the acoustic wave medium time-space domain Gaussian beam offset imaging technology based on the main frequency approximation, provides high-precision imaging guarantee for seismic data processing of complex structural areas, has less time cost consumption, and improves the quality and efficiency of subsequent interpretation work.
Drawings
FIG. 1 is a schematic flow chart provided in an embodiment of the present disclosure;
FIG. 2 is a depression true P-wave velocity model;
FIG. 3 is a conventional frequency domain Gaussian beam shift imaging result (depression model);
FIG. 4 is a conventional time-space domain Gaussian beam shift imaging result (depression model);
FIG. 5 is a graph showing imaging results (a depression model) provided by embodiments of the present disclosure;
FIG. 6 is a Marmousi true P-wave velocity model;
FIG. 7 is a conventional frequency domain Gaussian beam shift imaging result (Marmousi model);
FIG. 8 is a conventional time-space domain Gaussian beam shift imaging result (Marmousi model);
FIG. 9 is an image result (Marmousi model) provided in the example of the present specification;
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present application based on the embodiments herein.
First, the dominant frequency approximation method and flow used in the embodiments of the present specification will be specifically explained.
Forward wave field W in the space-time domain characterized by Gaussian beams (F) (x 0 ,t；x s ) The method can be written as follows:
where τ is the travel time of the central ray, x 0 ＝(x 0 ,z 0 ) Representing the spatial coordinates of the imaging point, x s ＝(x s 0) represents the spatial coordinates of the shot point, ω is the angular frequency and ε is the initial beam parameter. P(s) and Q(s) are based on observed data and the initial velocity v of the seismic wave propagating in the subsurface medium 0 And a kinetic ray tracing equationN is the transverse component of the ray.
Based on this, the dominant frequency of the observed data can be utilized to simplify the calculation of the forward wavefield with respect to the imaginary part at journey time:
wherein,and->Representing the real and imaginary parts of the forward wavefield, respectively, and ε is the representation of the dominant frequency approximation in the present invention.
Substituting equation (2) into equation (1) yields W (F) (x 0 ,t；x s ) Is an approximation of the expression:
wherein,
wherein omega m Is the dominant frequency of the observed data.
Fourier transforming equation (3) to obtain:
then, we use the up-going ray tracing strategy to construct a time-back wave field, record the observation data P U (x r T) from the acceptance point x r To subsurface imaging point x 0 May be realized with Kirchhoff integration:
wherein G (x) r ,t；x 0 ,t 0 ) Is a green's function, which is a ray-shifting squareIn the method, an important wave field mapping function between a seismic source and a receiving point is adopted. The green function is approximated by superposition of a series of gaussian beams:
under the condition of high frequency approximation, the derivative expression of the green function can be further simplified as:
wherein p is z Representing the vertical ray slowness of the receiving points.
Substituting equations (7), (8) into equation (6) we can obtain an expression of the counter-propagating time wavefield:
we obtain the frequency domain part, W, by fourier transforming the time domain representation part on the right side of equation (9) (R) (x 0 ,t 0 ) Can be re-expressed as:
likewise, the dominant frequency of the observed data is used to simplify the computation of the backward wavefield with respect to the imaginary part at journey time:
wherein, the expression of the dominant frequency approximation in the invention is approximately represented.
Substituting formula (11) into formula (10), W (R) (x 0 ,t 0 ) Can be further simplified into:
wherein,
the inverse fourier transform of equation (12) can be obtained:
wherein,
g(x r ,t)＝∫ωP U (x r ,ω)exp(iωt)dω (15)
finally, the imaging result is calculated using the cross-correlation imaging conditions:
substituting the formulas (5) and (14) into the formula (16) to obtain:
for the angle interval between the uplink rays and the downlink rays, we refer to the angular interval expression of the central rays of the traditional frequency domain Gaussian beam offset imaging method:
wherein omega ref Is the reference frequency, omega, of the observed data hig Is the highest frequency of the observed data.
In the round robin algorithm, we need to calculate the number of up and down rays:
wherein a is max Is the maximum offset angle, a min Is the minimum offset angle. In this way, a more convenient iterative summation can be performed during the imaging process based on the dominant frequency of the observed data.
The above section specifically describes the principle of dominant frequency approximation and imaging procedure employed in the embodiments of the present specification. Based on the foregoing, an embodiment of the present disclosure provides a method for performing time-space domain gaussian beam offset imaging on an acoustic medium based on dominant frequency approximation, as shown in fig. 1, where the process specifically includes the following steps:
s101, inputting a speed parameter field v and observed data P U (x r T), wherein x r Representing the space coordinates of the receiving points, t being the time of propagation of the seismic waves;
s103, determining the dominant frequency omega of the observed data m ；
S105, determining the forward wave field W by approximating the dominant frequency according to the velocity parameter field v (F) (x 0 ,t；x s ) And time-counter-propagating wave field W (R) (x 0 ,t 0 ) Wherein x is 0 Representing the spatial coordinates of the imaging point, x s Representing the space coordinates of the shot point, t is time, t 0 Is the initial value of the wave field propagation.
Specifically, the imaginary part of the forward wavefield is determined using the following formula:
wherein,is the imaginary part of the forward wave field, +.>For the attitude, im represents the imaginary part of the complex number,is the real part of the forward wave field travel time, re represents the real part of the complex number, τ is the initial value of the forward wave field travel time,representing the amplitude of the forward wavefield, delta represents the pulse function, s is the arc length component of the ray, s 0 Is the initial value of the ray arc length component, ω is the angular frequency, ε is the initial beam parameters, P(s) and Q(s) are the kinetic ray tracing equationsV of basic solution of (2) 0 Is the initial velocity of the seismic wave propagating in the subsurface medium, n is the transverse component of the ray;
and determining the imaginary part of the time-back-transmitted wavefield using the formula:
wherein,is the imaginary part of the time counter-transmitted wave field travel time, < >>Is the real part of the time counter-propagating wave field travel time, < >>Is the conjugate of the real part of the amplitude of the time-anti-transmitted wave field, g (x r T) is a custom parameter.
S107, performing cross-correlation according to the imaginary parts of the forward wave field and the time anti-transmission wave field to generate an imaging result.
That is, the imaging result is generated using the following cross-correlation formula:
wherein I (x) 0 ) To at the space point x 0 P z Is the vertical ray slowness of the receiving point.
Compared with the prior art, the method not only can maintain imaging precision and resolution which are compared with the traditional time-space domain Gaussian beam deviation method, but also can greatly improve the realization efficiency of the time-space domain Gaussian beam deviation method. The method develops the acoustic wave medium time-space domain Gaussian beam offset imaging technology based on the main frequency approximation, provides high-precision imaging guarantee for seismic data processing of complex structural areas, has less time cost consumption, and improves the quality and efficiency of subsequent interpretation work.
A description is given below of the actual effect of the embodiment in the model.
The method provided by the invention is firstly applied to a simple depression model imaging, and a relatively ideal imaging effect is obtained. A true velocity model (as shown in fig. 2); establishing a mobile receiving observation system, and inputting an observation gun record obtained by a smooth P wave velocity field and linear forward modeling; the forward wave field and the backward wave field are cross-correlated by adopting a cross-correlation imaging condition to obtain a traditional frequency domain Gaussian beam shift imaging result (shown in figure 3), a traditional time-space domain Gaussian beam shift imaging result (shown in figure 4) and an imaging result (shown in figure 5) provided by the embodiment of the specification.
In fig. 3, the frequency domain gaussian beam shift method produces some shift artefacts (red arrows). In fig. 4 and 5, we can see that, since the time-series back-propagation wave field adopts the up-ray tracing strategy, all reflection interfaces can be clearly imaged with comparable accuracy. Compared with the running time of two time-space domain Gaussian beam offset methods, the imaging method provided by the embodiment of the specification can improve the calculation efficiency of the depression model (figure 2) by 136.0 times.
The method provided by the invention is finally applied to the imaging of the international standard Marmousi model, and good imaging effect is obtained. A true velocity model (as shown in fig. 6); establishing a mobile receiving observation system, and inputting an observation gun record obtained by a smooth P wave velocity field and linear forward modeling; the forward wave field and the backward wave field are cross-correlated by adopting a cross-correlation imaging condition to obtain a traditional frequency domain Gaussian beam shift imaging result (shown in figure 7), a traditional time-space domain Gaussian beam shift imaging result (shown in figure 8) and an imaging result (shown in figure 9) provided by the embodiment of the specification.
It can be seen that the two time-space domain gaussian beam shift methods have higher imaging quality for the shallow layer than the frequency domain gaussian beam shift method (as shown in the parts of figures 7, 8, 9 blue ellipses and red rectangles). As can be seen from fig. 8 and 9, the imaging accuracy of the two time-space domain gaussian beam shift methods is approximately the same. Compared with the running time of two time-space domain Gaussian beam offset methods, the imaging method provided by the embodiment of the specification can improve the efficiency of a Marmousi model (figure 7) by 39.9 times.
The imaging method provided by the embodiment of the specification greatly improves the calculation efficiency of the traditional time-space domain Gaussian beam deviation method while guaranteeing the imaging precision and resolution, provides favorable conditions for development and application of the time-space domain Gaussian beam deviation imaging method, provides a more accurate imaging foundation for interpretation work of complex structural areas, and provides powerful technical support for secondary exploration and development of old oil fields.
Correspondingly, the embodiment of the application also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the method for imaging the time-space domain Gaussian beam offset of the acoustic wave medium based on the dominant frequency approximation.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus, device and medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and the relevant parts will be referred to in the description of the method embodiments, which is not repeated herein.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps or modules recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Claims (2)
1. A main frequency approximation-based acoustic wave medium time-space domain Gaussian beam offset imaging method comprises the following steps:
input speed parameter field v and observation data P U (x r T), wherein x r Representing the space coordinates of the receiving points, t being the time of propagation of the seismic waves;
determining the dominant frequency omega of the observed data m ；
From the velocity parameter field v, a forward wave field W is determined by approximation to the dominant frequency (F) (x 0 ,t；x s ) And time-counter-propagating wave field W (R) (x 0 ,t 0 ) Wherein x is 0 Representing the spatial coordinates of the imaging point, x s Representing the space coordinates of the shot point, t is time, t 0 Is the initial value of wave field propagation;
performing cross-correlation according to the imaginary parts of the forward wave field and the time counter wave field to generate an imaging result;
wherein determining the imaginary part of the forward wavefield and the time-back wavefield by the dominant frequency approximation comprises:
the imaginary part of the forward wavefield is determined using the following formula:
wherein,is the imaginary part of the forward wave field, +.>For the attitude, im represents the imaginary part of the complex number,is the real part of the forward wave field travel time, re represents the real part of the complex number, τ is the initial value of the forward wave field travel time,representing the amplitude of the forward wavefield, delta represents the pulse function, s is the arc length component of the ray, s 0 Is the initial value of the ray arc length component, ω is the angular frequency, ε is the initial beam parameters, P(s) and Q(s) are the kinetic ray tracing equationsV of basic solution of (2) 0 Is the initial velocity of the seismic wave propagating in the subsurface medium, n is the transverse component of the ray;
and determining the imaginary part of the time-back-transmitted wavefield using the formula:
wherein,is the imaginary part of the time counter-transmitted wave field travel time, < >>Is the real part of the time counter-propagating wave field travel time, < >>Is the conjugate of the real part of the amplitude of the time-anti-transmitted wave field, g (x r T) is a custom parameter;
correspondingly, performing cross-correlation according to the imaginary parts of the forward wave field and the time anti-transmission wave field to generate an imaging result, wherein the method comprises the following steps: generating an imaging result by adopting the following cross-correlation formula:
wherein I (x) 0 ) To at the space point x 0 P z Is the vertical ray slowness of the receiving point.
2. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of claim 1 when executing the program.
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| A Space-Time Self-Adaptive Gaussian Beam Migration with Optimized Dynamic Beam Parameter;Q.D. Lv 等;80th EAGE Conference and Exhibition 2018;1-5 * |
| 二维弹性多波时空域高斯束偏移方法;胡自多 等;地球物理学报(第02期);272-285 * |
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