阅读说明：本技术 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 (Sound wave medium time-space domain Gaussian beam migration imaging method based on main frequency approximation ) 是由 张东林 黄建平 杨继东 于 2021-08-13 设计创作，主要内容包括：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 the specification discloses a sound wave medium time-space domain Gaussian beam migration imaging method based on main frequency approximation. Inputting a speed parameter field and observation data; determining a dominant frequency of the observation data; determining the imaginary parts of a forward wave field and a time backward wave field by approximating the main frequency according to the speed parameter field; and carrying out cross correlation according to the imaginary parts of the forward wave field and the time backward wave field to generate an imaging result. The method can keep the imaging precision and resolution ratio which are similar to those of the traditional time-space domain Gaussian beam migration method, greatly improves the realization efficiency of the time-space domain Gaussian beam migration method, provides high-precision imaging guarantee for the seismic data processing of a complex structure area, has less time cost consumption, and improves the quality and efficiency rate of the subsequent interpretation work.)

1. A method for time-space domain Gaussian beam migration imaging of an acoustic medium based on main frequency approximation comprises the following steps:

inputting a velocity parameter field v and observation data P_U(x_rT), wherein x_rRepresenting the space coordinates of the receiving point, and t is the propagation time of the seismic wave;

determining a dominant frequency ω of the observation data_m；

Determining a forward wave field W by approximating the dominant frequency from the velocity parameter field v^(F)(x₀,t；x_s) Sum time backward wave field W^(R)(x₀,t₀) In which x is₀Spatial coordinates, x, representing the imaged points_sSpace coordinates representing the shot point, t being time, t₀An initial value for the wavefield propagation;

and carrying out cross correlation according to the imaginary parts of the forward wave field and the time backward wave field to generate an imaging result.

2. The method of claim 1, wherein determining the imaginary parts of the forward wavefield and the time-backward wavefield by a dominant frequency approximation comprises:

the imaginary component of the forward wavefield is determined using the following equation:

wherein the content of the first and second substances,as the imaginary part of the forward wavefield,in terms of attitude, Im represents the imaginary part of the complex number,is the real part of the travel time of the forward wave field, Re represents the real part of the complex number, tau is the initial value of the travel time of the forward wave field,representing the amplitude of the forward wavefield, δ representing the pulse function, s being the arc length component of the ray, s₀Is the initial value of the ray arc length component, ω is the angular frequency, ε is the initial beam parameter, P(s) and Q(s) are the kinetic ray-tracing equationsBasic solution of v₀Is the initial velocity of seismic waves propagating in the underground medium, and n is the transverse component of rays;

and, determining the imaginary part of the time-counterpoise field using the formula:

wherein the content of the first and second substances,is the imaginary part of the travel time of the time-reflection wave field,is the real part of the travel time of the time-domain back-propagation field,is the conjugate of the real part of the amplitude of the time-echo field, g (x)_rT) is a self-defined parameter;

correspondingly, cross-correlating the imaginary parts of the forward wavefield and the time-backward wavefield to generate an imaging result, comprising: generating an imaging result using the following cross-correlation equation:

wherein, I (x)₀) To be at a spatial point x₀As a result of the imaging of (1), p_zIs the vertical ray slowness of the receive point.

3. 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.

Technical Field

The specification relates to the field of exploration geophysics, in particular to a sound wave medium time-space domain Gaussian beam offset imaging method based on main frequency approximation.

Background

Conventional frequency domain gaussian beam migration methods may produce weak illumination and strong artifacts in complex deep structures due to inaccuracies in paraxial ray tracing of the backward wavefield. And for the traditional time-space domain Gaussian beam migration method, the imaging precision is better, but the calculation cost is higher. 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 method of imaging in deep structures.

Disclosure of Invention

The invention aims to provide an accurate and stable imaging method in a deep structure.

In order to solve the technical problems, the invention adopts the following technical scheme:

inputting a velocity parameter field v and observation data P_U(x_rT), wherein x_rRepresenting the space coordinates of the receiving point, and t is the propagation time of the seismic wave;

determining a dominant frequency ω of the observation data_m；

Determining a forward wave field W by approximating the dominant frequency from the velocity parameter field v^(F)(x₀,t；x_s) Sum time backward wave field W^(R)(x₀,t₀) In which x is₀Spatial coordinates, x, representing the imaged points_sSpace coordinates representing the shot point, t being time, t₀An initial value for the wavefield propagation;

and carrying out cross correlation according to the imaginary parts of the forward wave field and the time backward wave field to generate an imaging result.

The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:

inputting a speed parameter field and observation data; determining a dominant frequency of the observation data; determining the imaginary parts of a forward wave field and a time backward wave field by approximating the main frequency according to the speed parameter field; and carrying out cross correlation according to the imaginary parts of the forward wave field and the time backward wave field to generate an imaging result. Compared with the prior art, the method can keep the imaging precision and resolution ratio which are similar to those of the traditional time-space domain Gaussian beam migration method, and greatly improves the realization efficiency of the time-space domain Gaussian beam migration method. The acoustic medium time-space domain Gaussian beam migration imaging technology based on the main frequency approximation is developed, high-precision imaging guarantee is provided for seismic data processing of a complex structure area, time cost consumption is reduced, and quality and efficiency of subsequent interpretation work are improved.

Drawings

FIG. 1 is a schematic flow chart provided by an embodiment of the present disclosure;

FIG. 2 is a dimpled true P-wave velocity model;

FIG. 3 shows the result of conventional frequency domain Gaussian beam shift imaging (dimple model);

FIG. 4 is a plot of conventional time-space domain Gaussian beam shift imaging results (dimple model);

FIG. 5 is an imaging result (dimple model) provided by an embodiment of the present description;

FIG. 6 is a Marmousi true P-wave velocity model;

FIG. 7 shows the result of conventional frequency domain Gaussian beam offset imaging (Marmousi model);

FIG. 8 is a conventional time-space domain Gaussian beam offset imaging result (Marmousi model);

fig. 9 shows imaging results (Marmousi model) provided in an embodiment of the present description;

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step are within the scope of the present application.

First, a main frequency approximation method and a flow used in the embodiments of the present specification are specifically explained.

Forward wavefield W in the space-time domain characterized by a Gaussian beam^(F)(x₀,t；x_s) Can be written as:

wherein, tau is the travel time of the central ray, x₀＝(x₀,z₀) Spatial coordinates, x, representing the imaged points_s＝(x_s0) 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 initial velocities v of propagation of seismic waves in the subsurface medium based on the observed data₀And dynamic ray tracing equationN is the lateral component of the ray.

Based on this, the dominant frequency of the observed data can be used to simplify the calculation of the forward wavefield in relation to the imaginary part of the travel time:

wherein the content of the first and second substances,andrespectively representing the real part and the imaginary part of a forward wave field, and is approximately represented by the dominant frequency in the invention.

Substituting equation (2) into equation (1) yields W^(F)(x₀,t；x_s) Approximate expression of (c):

wherein the content of the first and second substances,

wherein, ω is_mIs the dominant frequency of the observed data.

Fourier transforming equation (3) into:

then, the up-ray tracing strategy is used for constructing a time-reversal wave field and recording observation data P_U(x_rT) from the reception point x_rTo subsurface imaging point x₀The back propagation of (a) can be achieved with Kirchhoff integration:

wherein, G (x)_r,t；x₀,t₀) Is the green's function, which is an important wavefield mapping function between source and receiver points in the ray migration method. The green function is approximated by a 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 follows:

wherein p is_zRepresenting the vertical ray slowness of the receiving point.

By substituting equations (7) and (8) into equation (6), we can obtain the expression of the backward time wave field:

we obtain the frequency domain part, W, by Fourier transformation of the right-hand time domain representation of equation (9)^(R)(x₀,t₀) Can be re-expressed as:

likewise, the dominant frequency of the observed data is used to simplify the calculation of the backward wavefield that is related to the imaginary part of the travel time:

wherein, the approximation of the main frequency is embodied in the invention.

Substituting formula (11) into formula (10), W^(R)(x₀,t₀) Can be further simplified to:

wherein the content of the first and second substances,

by inverse fourier transforming equation (12), we can obtain:

wherein the content of the first and second substances,

g(x_r,t)＝∫ωP_U(x_r,ω)exp(iωt)dω (15)

finally, the imaging result is calculated by using the cross-correlation imaging conditions:

substituting equations (5) and (14) into equation (16) to obtain:

for the angle interval of the uplink ray and the downlink ray, the expression of the angle interval of the central ray in the traditional frequency domain Gaussian beam offset imaging method is used for reference:

wherein, ω is_refIs the reference frequency, ω, of the observed data_higIs the highest frequency of observed data.

In the round-robin algorithm, we need to compute the number of ascending and descending rays:

wherein, a_maxIs the maximum deviation angle, a_minIs the minimum offset angle. By the method, more convenient iterative summation can be carried out in the imaging process based on the main frequency of the observation data.

The above section specifically explains the principle of dominant frequency approximation and the imaging procedure adopted in the embodiments of the present specification. Based on the foregoing, embodiments of the present specification provide a method for time-space domain gaussian beam shift imaging of an acoustic wave medium based on dominant frequency approximation, as shown in fig. 1, the method specifically includes the following steps:

s101, inputting a speed parameter field v and observation data P_U(x_rT), wherein x_rRepresenting the space coordinates of the receiving point, and t is the propagation time of the seismic wave;

s103, determining the main frequency omega of the observation data_m；

S105, according to the speed parameter field v, determining a forward wave field W by approximating a main frequency^(F)(x₀,t；x_s) Sum time backward wave field W^(R)(x₀,t₀) In which x is₀Spatial coordinates, x, representing the imaged points_sSpace coordinates representing the shot point, t being time, t₀Is a waveThe initial value of the field propagation.

Specifically, the imaginary part of the forward wavefield is determined using the following equation:

wherein the content of the first and second substances,as the imaginary part of the forward wavefield,in terms of attitude, Im represents the imaginary part of the complex number,is the real part of the travel time of the forward wave field, Re represents the real part of the complex number, tau is the initial value of the travel time of the forward wave field,representing the amplitude of the forward wavefield, δ representing the pulse function, s being the arc length component of the ray, s₀Is the initial value of the ray arc length component, ω is the angular frequency, ε is the initial beam parameter, P(s) and Q(s) are the kinetic ray-tracing equationsBasic solution of v₀Is the initial velocity of seismic waves propagating in the underground medium, and n is the transverse component of rays;

and, determining the imaginary part of the time-counterpoise field using the formula:

wherein the content of the first and second substances,is the imaginary part of the travel time of the time-reflection wave field,is the real part of the travel time of the time-domain back-propagation field,is the conjugate of the real part of the amplitude of the time-echo field, g (x)_rAnd t) is a self-defined parameter.

And S107, performing cross correlation according to the imaginary parts of the forward wave field and the time backward wave field to generate an imaging result.

Namely, the imaging result is generated by adopting the following cross-correlation formula:

wherein, I (x)₀) To be at a spatial point x₀As a result of the imaging of (1), p_zIs the vertical ray slowness of the receive point.

Compared with the prior art, the method can keep the imaging precision and resolution ratio which are similar to those of the traditional time-space domain Gaussian beam migration method, and greatly improves the realization efficiency of the time-space domain Gaussian beam migration method. The acoustic medium time-space domain Gaussian beam migration imaging technology based on the main frequency approximation is developed, high-precision imaging guarantee is provided for seismic data processing of a complex structure area, time cost consumption is reduced, and quality and efficiency of subsequent interpretation work are improved.

The following description is given as an explanation of the practical effects of the embodiment in the model.

The method provided by the invention is firstly applied to a simple hollow model imaging, and a more ideal imaging effect is obtained. A true velocity model (as shown in FIG. 2); establishing a mobile receiving observation system, and inputting a smooth P-wave velocity field and an observation shot record obtained by linear forward modeling; and cross-correlating the forward and backward wave fields by using a cross-correlation imaging condition to obtain a traditional frequency domain Gaussian beam shift imaging result (shown in FIG. 3), a traditional time-space domain Gaussian beam shift imaging result (shown in FIG. 4), and an imaging result provided by the embodiment of the specification (shown in FIG. 5).

In fig. 3, the frequency domain gaussian beam shifting method produces some shift artifacts (red arrows). In fig. 4 and 5, we can see that all reflection interfaces can be clearly imaged with equal accuracy due to the up-ray tracing strategy adopted by the time-backpass wave field. Comparing the running times of the two time-space domain gaussian beam shift methods, the imaging method provided by the embodiment of the present specification can improve the calculation efficiency of the hollow model (fig. 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 achieved. A true velocity model (as shown in FIG. 6); establishing a mobile receiving observation system, and inputting a smooth P-wave velocity field and an observation shot record obtained by linear forward modeling; and cross-correlating the forward and backward wave fields by using a cross-correlation imaging condition to obtain a traditional frequency domain Gaussian beam shift imaging result (shown in FIG. 7), a traditional time-space domain Gaussian beam shift imaging result (shown in FIG. 8), and an imaging result provided by the embodiment of the specification (shown in FIG. 9).

It can be seen that the imaging quality of the shallow layers by the two time-space domain gaussian beam shifting methods is higher than that by the frequency domain gaussian beam shifting method (as shown in fig. 7, 8, 9 blue oval and red rectangle). 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. Comparing the running times of the two time-space domain gaussian beam migration methods, the imaging method provided by the embodiment of the present specification can improve the efficiency of the Marmousi model (fig. 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 migration method while ensuring the imaging precision and resolution, provides favorable conditions for the development and application of the time-space domain Gaussian beam migration imaging method, provides a more accurate imaging basis for the interpretation work of a complex structure area, and provides a powerful technical support for the secondary exploration and development of the old oil field.

Correspondingly, the embodiment of the present application further provides a computer device, the device includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method of time-space domain gaussian beam shift imaging based on main frequency approximation for acoustic wave medium as described above.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Especially, as for the device, apparatus and medium type embodiments, since they are basically similar to the method embodiments, the description is simple, and the related points may refer to part of the description of the method embodiments, which is not repeated here.

The foregoing description has been directed to specific embodiments of this 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 may also be possible or may be advantageous.