阅读说明：本技术 一种高频重建方法、装置及计算机存储介质 (High-frequency reconstruction method and device and computer storage medium ) 是由 侯献华 王伟 樊馥 苏奎 邢恩袁 于 2019-10-09 设计创作，主要内容包括：本发明公开了一种高频重建方法、装置及计算机存储介质,首先通过测井数据确定反射系数序列；接着根据所获得的反射系数序列和过井原始剖面地震道数据确定过井合成地震记录空间数据；进一步地利用高斯核模型根据所述过井合成地震记录空间数据和原始地震道数据,计算高频地震记录；最后根据所计算得到的高频地震记录和所述原始地震道数据确定高频重建结果,所述高频重建结果中的高频部分保持所述过井合成地震记录空间数据的高频特性,所述高频重建结果中的低频部分保持所述原始地震道数据的低频特征。(The invention discloses a high-frequency reconstruction method, a device and a computer storage medium, firstly, a reflection coefficient sequence is determined through logging data; determining well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic channel data; further utilizing a Gaussian kernel model to calculate high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic trace data; and finally, determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, wherein the high-frequency part in the high-frequency reconstruction result keeps the high-frequency characteristic of the spatial data of the well-crossing synthetic seismic record, and the low-frequency part in the high-frequency reconstruction result keeps the low-frequency characteristic of the original seismic channel data.)

1. A high frequency reconstruction method, characterized in that the method comprises:

determining a sequence of reflection coefficients from the well log data;

determining well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic channel data;

calculating high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic channel data by using a Gaussian kernel model;

and determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, wherein the high-frequency part in the high-frequency reconstruction result keeps the high-frequency characteristic of the spatial data of the well-crossing synthetic seismic record, and the low-frequency part in the high-frequency reconstruction result keeps the low-frequency characteristic of the original seismic channel data.

2. The method of claim 1, wherein determining the sequence of reflection coefficients from the well log data comprises:

extracting acoustic time difference sequence and density data by using the logging data;

and performing dot product operation on the sound wave time difference sequence and the density data to obtain a reflection coefficient sequence.

3. The method of claim 1, wherein determining well-through synthetic seismic record spatial data from the obtained sequence of reflection coefficients and well-through raw profile seismic trace data comprises:

extracting a well side channel seismic wavelet sequence from the well-crossing original profile seismic channel data;

and performing convolution operation by using the extracted well side channel seismic wavelet sequence and the reflection coefficient sequence to obtain well-through synthetic seismic record spatial data.

4. The method of claim 1, wherein computing high frequency seismic records from the well-through synthetic seismic record spatial data and raw seismic trace data using a gaussian kernel model comprises:

synthesizing seismic record space data by using the well-crossing, and obtaining a Gaussian kernel function with high-frequency components by a least square method;

acquiring a parameter vector matched with the original seismic channel by using the original seismic channel data;

and adding the products of the Gaussian kernel functions corresponding to different seismic channels and the parameter vectors to obtain the high-frequency seismic record.

5. The method of claim 1, wherein determining a high frequency reconstruction result from the computed high frequency seismic records and the raw seismic trace data comprises:

carrying out high-pass filtering on the high-frequency seismic record to obtain high-frequency part data;

and adding the original seismic channel data and the high-frequency part data to obtain a high-frequency reconstruction result.

6. A high frequency reconstruction apparatus, characterized in that the apparatus comprises:

the first determination module is used for determining a reflection coefficient sequence through logging data;

the second determination module is used for determining the well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic channel data;

the calculation module is used for calculating high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic channel data by utilizing a Gaussian kernel model;

and the third determination module is used for determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, wherein the high-frequency part in the high-frequency reconstruction result keeps the high-frequency characteristic of the spatial data of the well-crossing synthetic seismic record, and the low-frequency part in the high-frequency reconstruction result keeps the low-frequency characteristic of the original seismic channel data.

7. The apparatus of claim 6,

the first determining module is specifically configured to extract acoustic time difference sequences and density data by using the logging data; and performing dot product operation on the sound wave time difference sequence and the density data to obtain a reflection coefficient sequence.

8. The apparatus of claim 6,

the second determining module is specifically used for extracting a well side channel seismic wavelet sequence from the well-crossing original profile seismic channel data; and performing convolution operation by using the extracted well side channel seismic wavelet sequence and the reflection coefficient sequence to obtain well-through synthetic seismic record spatial data.

9. The apparatus of claim 6,

the calculation module is specifically used for obtaining a Gaussian kernel function with high-frequency components by using the well-crossing synthetic seismic record spatial data through a least square method; acquiring a parameter vector matched with the original seismic channel by using the original seismic channel data; and adding the products of the Gaussian kernel functions corresponding to different seismic channels and the parameter vectors to obtain the high-frequency seismic record.

10. A computer storage medium comprising a set of computer executable instructions for performing the high frequency reconstruction method of any one of claims 1 to 5 when executed.

Technical Field

The present invention relates to seismic surveying, and more particularly, to a high frequency reconstruction method, apparatus, and computer storage medium.

Background

In recent years, the application of high frequency information recovery technology to seismic data processing has been highly emphasized. The conventional academic high-frequency reconstruction method mainly comprises sparse pulse inversion, spectrum continuation, spectrum whitening, histogram correction, self-modulation high-frequency reconstruction and the like. The self-adjusting high-frequency reconstruction method for the comparative leading edge mainly aims to lift up the extreme point of the seismic data to obtain a modulation function, then multiplies a high-frequency component obtained by the modulation function through a power absolute value by the original seismic data, and finally replaces a low-frequency component of the original seismic data with a low-frequency part of an obtained result to obtain the final high-frequency recovery seismic data.

However, the self-adjusting high frequency reconstruction method described above has the following drawbacks: 1) the method depends on well data and well-passing profile information excessively, the obtained frequency expansion data wave group detail information is not rich, the characteristic that seismic profiles are thick and thin in thickness cannot be shown due to the fact that the obtained frequency expansion data wave group is only in the mode of homophase axis thinning, and interlayer information of the original profile, particularly coarse particle reservoir information in sylvite, is lost; (2) the method is suitable for a layered model and is not suitable for seismic data of reservoirs with severe stratum and structure transverse change; (3) the effect is poor for fault-controlled potassium salt reservoirs.

Disclosure of Invention

In order to overcome the above defects of the current high-frequency reconstruction method, embodiments of the present invention creatively provide a new high-frequency reconstruction method, apparatus, and computer storage medium.

According to a first aspect of the present invention, there is provided a high frequency reconstruction method, the method comprising: determining a sequence of reflection coefficients from the well log data; determining well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic channel data; calculating high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic channel data by using a Gaussian kernel model; and determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, wherein the high-frequency part in the high-frequency reconstruction result keeps the high-frequency characteristic of the spatial data of the well-crossing synthetic seismic record, and the low-frequency part in the high-frequency reconstruction result keeps the low-frequency characteristic of the original seismic channel data.

According to an embodiment of the present invention, the determining the sequence of reflection coefficients from the well log data includes: extracting acoustic time difference sequence and density data by using the logging data; and performing dot product operation on the sound wave time difference sequence and the density data to obtain a reflection coefficient sequence.

According to one embodiment of the invention, the determining the well-through synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-through original profile seismic trace data comprises: extracting a well side channel seismic wavelet sequence from the well-crossing original profile seismic channel data; and performing convolution operation by using the extracted well side channel seismic wavelet sequence and the reflection coefficient sequence to obtain well-through synthetic seismic record spatial data.

According to an embodiment of the invention, the calculating the high-frequency seismic record according to the well-crossing synthetic seismic record space data and the original seismic trace data through the gaussian kernel model comprises: synthesizing seismic record space data by using the well-crossing, and obtaining a Gaussian kernel function with high-frequency components by a least square method; acquiring a parameter vector matched with the original seismic channel by using the original seismic channel data; and adding the products of the Gaussian kernel functions corresponding to different seismic channels and the parameter vectors to obtain the high-frequency seismic record.

According to an embodiment of the present invention, the determining a high frequency reconstruction result according to the calculated high frequency seismic record and the original seismic trace data includes: carrying out high-pass filtering on the high-frequency seismic record to obtain high-frequency part data; and adding the original seismic channel data and the high-frequency part data to obtain a high-frequency reconstruction result.

According to a second aspect of the present invention, there is also provided a high frequency reconstruction apparatus, the apparatus comprising: the first determination module is used for determining a reflection coefficient sequence through logging data; the second determination module is used for determining the well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic channel data; the calculation module is used for calculating high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic channel data by utilizing a Gaussian kernel model; and the third determination module is used for determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, wherein the high-frequency part in the high-frequency reconstruction result keeps the high-frequency characteristic of the spatial data of the well-crossing synthetic seismic record, and the low-frequency part in the high-frequency reconstruction result keeps the low-frequency characteristic of the original seismic channel data.

According to an embodiment of the present invention, the first determining module is specifically configured to extract acoustic time difference sequence and density data by using the logging data; and performing dot product operation on the sound wave time difference sequence and the density data to obtain a reflection coefficient sequence.

According to an embodiment of the present invention, the second determining module is specifically configured to extract a well side channel seismic wavelet sequence from the well-crossing original profile seismic channel data; and performing convolution operation by using the extracted well side channel seismic wavelet sequence and the reflection coefficient sequence to obtain well-through synthetic seismic record spatial data.

According to an embodiment of the invention, the calculation module is specifically configured to obtain a gaussian kernel function with high frequency components by a least square method using the well-crossing synthetic seismic record spatial data; acquiring a parameter vector matched with the original seismic channel by using the original seismic channel data; and adding the products of the Gaussian kernel functions corresponding to different seismic channels and the parameter vectors to obtain the high-frequency seismic record.

According to an embodiment of the present invention, the third determining module is specifically configured to perform high-pass filtering on the high-frequency seismic record to obtain high-frequency partial data; and adding the original seismic channel data and the high-frequency part data to obtain a high-frequency reconstruction result.

According to a third aspect of embodiments of the present invention, there is provided a computer storage device comprising a set of computer executable instructions for performing any of the above-mentioned high frequency reconstruction methods when the instructions are executed.

According to the high-frequency reconstruction method, the high-frequency reconstruction device and the computer storage medium, firstly, a reflection coefficient sequence is determined through logging data; determining well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic channel data; further utilizing a Gaussian kernel model to calculate high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic trace data; and finally, determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, wherein the high-frequency part in the high-frequency reconstruction result keeps the high-frequency characteristic of the spatial data of the well-crossing synthetic seismic record, and the low-frequency part in the high-frequency reconstruction result keeps the low-frequency characteristic of the original seismic channel data. Therefore, the method and the process for well-constrained seismic high-resolution processing based on the Gaussian kernel model are suitable for noise data; in addition, in the Gaussian kernel model, fitting approximation is carried out near the parameter sample space, so that the influence of dimension disaster is avoided. In addition, the high-frequency reconstruction result obtained by the method not only keeps the low-frequency characteristic and the transverse change characteristic of the original seismic section, but also has high-frequency components matched with the well, and the high-frequency expansion part can carry out frequency expansion processing on the coarse particle reservoir in the target layer by adjusting the parameter size, so that the seismic section after high-frequency reconstruction is beneficial to well seismic calibration and inversion.

It is to be understood that the teachings of the present invention need not achieve all of the above-described benefits, but rather that specific embodiments may achieve specific technical results, and that other embodiments of the present invention may achieve benefits not mentioned above.

Drawings

The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:

in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

FIG. 1 is a schematic diagram illustrating an implementation flow of a high-frequency reconstruction method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram showing the structure of data components for determining a reflection coefficient sequence from well log data according to an exemplary embodiment of the present invention;

FIG. 3 is a schematic diagram of the data structure of the synthetic seismic record space data determined by the invention;

FIG. 4 is a schematic diagram of an optimal parameter vector based on a Gaussian kernel model in an exemplary application of the present invention;

FIG. 5 is a diagram illustrating a Gaussian kernel function with high frequency components in an example application of the present invention;

FIG. 6 is a diagram illustrating a comparison between before and after high frequency reconstruction of single-channel seismic data according to an application example of the present invention;

FIG. 7 is a diagram illustrating a high frequency reconstruction pre-and post-contrast of multi-channel seismic data in another exemplary application of the present invention;

fig. 8 is a schematic diagram illustrating a composition structure of a high-frequency reconstruction apparatus according to an embodiment of the present invention.

Detailed Description

The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given only to enable those skilled in the art to better understand and to implement the present invention, and do not limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

The technical solution of the present invention is further elaborated below with reference to the drawings and the specific embodiments.

FIG. 1 is a schematic diagram illustrating an implementation flow of a high-frequency reconstruction method according to an embodiment of the present invention; FIG. 2 is a schematic diagram showing the structure of data components for determining a reflection coefficient sequence from well log data according to an exemplary embodiment of the present invention; FIG. 3 is a schematic diagram of the data structure of the synthetic seismic record space data determined by the invention; FIG. 4 is a schematic diagram of an optimal parameter vector based on a Gaussian kernel model in an exemplary application of the present invention; FIG. 5 is a diagram illustrating a Gaussian kernel function with high frequency components in an example application of the present invention; FIG. 6 is a diagram illustrating a comparison between before and after high frequency reconstruction of single-channel seismic data according to an application example of the present invention; FIG. 7 is a diagram illustrating a high frequency pre-and post-reconstruction comparison of multi-channel seismic data in an application example of the present invention.

Referring to fig. 1, a high frequency reconstruction method according to an embodiment of the present invention includes: operation 101, determining a reflection coefficient sequence through logging data; an operation 102 of determining a well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic trace data; operation 103, calculating high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic channel data by using a Gaussian kernel model; and operation 104, determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data.

Referring to FIG. 2, in operation 101, first extracting a sonic moveout sequence (shown on the left of FIG. 2) and density data (shown in FIG. 2) using the well log data; and then performing dot product operation on the sound wave time difference sequence and the density data to obtain a reflection coefficient sequence (as shown on the right of figure 2).

Referring to FIG. 3, at operation 102, a well side-channel seismic wavelet sequence (shown in the left one of FIG. 3) is first extracted from the well-through original profile seismic trace data (shown in the left two of FIG. 3); and performing convolution operation by using the extracted well side channel seismic wavelet sequence and the reflection coefficient sequence (as shown in the left five of the figure 3) to obtain well-through synthetic seismic recording space data.

In operation 103, a Gaussian kernel function having high frequency components is first obtained by a least squares method using the through-well synthetic seismic recording spatial data

Wherein x represents a gaussian kernel function input variable; c represents the mean of the gaussian kernel function; h represents the bandwidth of the Gaussian kernel function; obtaining and using the original seismic trace dataParameter vector theta of seismic trace matching is (theta)₁,θ₂,…,θ_b)^TWherein b represents the number of basis functions, the value of which does not depend on the dimension (seismic channel number) of an input variable x and depends on the discrete sampling point number of a vector x; adding the products of the Gaussian kernel functions and the parameter vectors corresponding to different seismic channels to obtain a high-frequency seismic record

Wherein j represents the number of factors and the corresponding mean value, and the value range of j is 1-b.

Specifically, step 1, the well-through synthetic seismic record space data is used as a training set sample space

n represents the number of seismic traces; step 2, setting the output of the training set sample space, namely the high-frequency seismic record asWherein the content of the first and second substances,

for high-frequency kernel function, θ ═ θ₁,θ₂,…,θ_b)^TIs a parameter vector; step 3, for n seismic data, establishing least square model learning,to solve the error minimum time parameter

Step 4, square error J_lsIn the form of (theta) matrixIs n × b matrix, theta ═ theta₁,θ₂,…,θ_b)^TIs a b column vector; step 5, as shown in fig. 4, obtaining the optimal parameters based on the gaussian kernel model by adjusting the bandwidth factor h of the gaussian kernel functionVector quantity; step 6, as shown in fig. 5, the gaussian kernel function corresponding to the optimal parameter vector is used as the high frequency reconstruction matrix, and each curve represents K_iThe translation parameter is C; and 7, continuously repeating the steps 1-4 on the original earthquake to obtain the parameter vector theta of each seismic channel_i＝(θ₁,θ₂,…,θ_b)_i ^TWherein i is a different seismic trace; step 8, the parameter vector theta matched by the original seismic trace is changed into (theta)₁,θ₂,…,θ_b)^TAnd (4) multiplying the obtained product by the Gaussian kernel function K determined in the step (4) to obtain the high-frequency seismic record. Therefore, the invention can obtain the parameter vector matched with the original seismic record, and the parameter vector corresponds to the wave crest, the wave trough and the zero point of the original seismic record, so that the frequency spectrum of the original seismic record can be expanded by multiplying the parameter vector by the Gaussian function with high-frequency characteristics.

In operation 104, first performing high-pass filtering on the high-frequency seismic record to obtain high-frequency part data; and adding the original seismic channel data and the high-frequency part data to obtain a high-frequency reconstruction result. Here, when the high frequency seismic record is high-pass filtered, its high-pass filtering parameter f_highGreater than primary frequency f of original seismic record_mainAnd a half bandwidth f_widthSum, i.e. f_high≥(f_main+f_width/2)。

Wherein a high-frequency part in the high-frequency reconstruction result maintains high-frequency characteristics of the well-crossing synthetic seismic recording spatial data, and a low-frequency part in the high-frequency reconstruction result maintains low-frequency characteristics of the original seismic trace data.

Referring to a schematic diagram of comparing before and after high-frequency reconstruction of single-channel seismic data in an application example shown in fig. 6 or a schematic diagram of comparing before and after high-frequency reconstruction of multi-channel seismic data in another application example shown in fig. 7, specifically referring to a seismic record before frequency extension, a frequency spectrum before frequency extension, a seismic record after frequency extension and a frequency spectrum after frequency extension which are sequentially arranged at the upper left, the lower left, the upper right and the lower right of fig. 6, it is not difficult to find that seismic record high-frequency compensation extends an unknown frequency spectrum by using a known frequency spectrum, and a gaussian kernel function with a high-frequency component adopted by the method corresponds to a synthesized record high frequency. Taking a Gaussian kernel function as a model, synthesizing and recording wave crests, wave troughs and zero points as initial parameter vectors, taking the product of the model and the parameter vectors as output aiming at the model, taking the synthesized record as an input target, obtaining the parameter vector corresponding to the minimum value of the output difference value and the input difference value under the norm condition, and obtaining the optimal parameter vector by adjusting different bandwidth factors in the Gaussian function, wherein the Gaussian kernel function corresponding to the parameter vector has the high-frequency spectrum of the synthesized seismic record.

According to the high-frequency reconstruction method, firstly, a reflection coefficient sequence is determined through logging data; determining well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic channel data; further utilizing a Gaussian kernel model to calculate high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic trace data; and finally, determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, wherein the high-frequency part in the high-frequency reconstruction result keeps the high-frequency characteristic of the spatial data of the well-crossing synthetic seismic record, and the low-frequency part in the high-frequency reconstruction result keeps the low-frequency characteristic of the original seismic channel data. Therefore, the method and the process for well-constrained seismic high-resolution processing based on the Gaussian kernel model are suitable for noise data; in addition, in the Gaussian kernel model, fitting approximation is carried out near the parameter sample space, so that the influence of dimension disaster is avoided. In addition, the high-frequency reconstruction result obtained by the method not only keeps the low-frequency characteristic and the transverse change characteristic of the original seismic section, but also has high-frequency components matched with the well, and the high-frequency expansion part can carry out frequency expansion processing on the coarse particle reservoir in the target layer by adjusting the parameter size, so that the seismic section after high-frequency reconstruction is beneficial to well seismic calibration and inversion.

Based on the above high frequency reconstruction method, an embodiment of the present invention further provides a high frequency reconstruction apparatus, as shown in fig. 8, where the apparatus 80 includes: a first determining module 801, configured to determine a sequence of reflection coefficients from well log data; a second determining module 802, configured to determine a synthetic seismic recording space data of the well according to the obtained reflection coefficient sequence and the seismic trace data of the original profile of the well; a calculation module 803, configured to calculate a high-frequency seismic record according to the well-crossing synthetic seismic record spatial data and the original seismic trace data by using a gaussian kernel model; a third determining module 804, configured to determine a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data, where a high-frequency portion in the high-frequency reconstruction result maintains high-frequency characteristics of the spatial data of the well-crossing synthetic seismic record, and a low-frequency portion in the high-frequency reconstruction result maintains low-frequency characteristics of the original seismic channel data.

According to an embodiment of the present invention, the first determining module 801 is specifically configured to extract acoustic time difference sequence and density data by using the logging data; and performing dot product operation on the sound wave time difference sequence and the density data to obtain a reflection coefficient sequence.

According to an embodiment of the present invention, the second determining module 802 is specifically configured to extract a well side channel seismic wavelet sequence from the well-crossing original profile seismic channel data; and performing convolution operation by using the extracted well side channel seismic wavelet sequence and the reflection coefficient sequence to obtain well-through synthetic seismic record spatial data.

According to an embodiment of the present invention, the calculation module 803 is specifically configured to obtain a gaussian kernel function with high frequency components by a least square method using the well-crossing synthetic seismic record spatial data; acquiring a parameter vector matched with the original seismic channel by using the original seismic channel data; and adding the products of the Gaussian kernel functions corresponding to different seismic channels and the parameter vectors to obtain the high-frequency seismic record.

According to an embodiment of the present invention, the third determining module 804 is specifically configured to perform high-pass filtering on the high-frequency seismic record to obtain high-frequency part data; and adding the original seismic channel data and the high-frequency part data to obtain a high-frequency reconstruction result.

Also, based on the high frequency reconstruction method as described above, an embodiment of the present invention further provides a computer-readable storage medium storing a program, which, when executed by a processor, causes the processor to perform at least the operation steps of: operation 101, determining a reflection coefficient sequence through logging data; an operation 102 of determining a well-crossing synthetic seismic record spatial data according to the obtained reflection coefficient sequence and the well-crossing original profile seismic trace data; operation 103, calculating high-frequency seismic records according to the well-crossing synthetic seismic record space data and the original seismic channel data by using a Gaussian kernel model; and operation 104, determining a high-frequency reconstruction result according to the calculated high-frequency seismic record and the original seismic channel data.

Here, it should be noted that: the above description of the embodiments of the high frequency reconstruction apparatus and the computer storage medium is similar to the description of the embodiments of the method shown in fig. 1 to 7, and has similar beneficial effects to the embodiments of the method shown in fig. 1 to 7, and therefore, the description thereof is omitted. For technical details not disclosed in the embodiment of the high frequency reconstruction device of the present invention, please refer to the description of the method embodiment shown in fig. 1 to 7 of the present invention for understanding, and therefore, for brevity, will not be described again.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.

Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.