CN115685340A - Method and device for improving seismic characterization precision of thin-layer structure - Google Patents
Method and device for improving seismic characterization precision of thin-layer structure Download PDFInfo
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Abstract
The invention relates to a method for improving seismic characterization precision of a thin-layer structure, which comprises the following steps: acquiring original seismic data; establishing a multi-channel deconvolution target function according to the original seismic data; obtaining a prior probability distribution function of the reflection coefficient, and applying probability distribution constraint to the reflection coefficient in the longitudinal direction; acquiring dip angle information, and constructing a space constraint item by using the dip angle information, wherein the constraint item comprises the dip angle information of a same phase axis in the whole seismic channel; and constructing a multi-channel deconvolution target functional based on the dip angle, and solving the multi-channel deconvolution target functional to obtain seismic record data with improved resolution. The method is suitable for earthquake prediction and description of complex reservoirs, can more effectively reflect thin reservoirs, has inversion results closer to actual geological features, can well make up for the defect of constraining sparse deconvolution, and realizes fine description of reservoir features.
Description
Technical Field
The invention belongs to the technical field of seismic data processing, interpretation and inversion of oil and gas geophysical exploration and development, and particularly relates to a method and a device for improving the seismic representation precision of a thin-layer structure.
Background
With the continuous and deep exploration and development of oil fields, the conventional seismic data cannot meet the requirements of complex geological condition oil and gas exploration and development due to the limitation of seismic resolution, especially the fine thin interbed interpretation, reservoir prediction and reservoir description of a complex target area. Therefore, improving the resolution of seismic data becomes a technical key for solving the problems of structure fine interpretation and reservoir prediction in the fine exploration and development stage of oil and gas.
Deconvolution is the primary method for improving the resolution of seismic data. The method recovers the underground real reflection coefficient sequence by compressing the seismic wavelets. However, conventional linear deconvolution is limited by the effective frequency band of the original seismic data and cannot recover reflection information outside the effective frequency band. In order to obtain seismic data with high resolution, many researchers have artificially assumed that the reflection coefficients satisfy some kind of function distribution, such as p-norm distribution, huber distribution, sech distribution, cauchy distribution, modified cauchy distribution, and the like, and these constraints can improve the resolution while maintaining a good signal-to-noise ratio. However, when the reflectance distribution characteristics of the actual work area are not consistent with the constraint criteria, the processing effect is greatly reduced. In addition, the sparsity constraint criterion has a certain suppression effect on weak reflector information, which is contrary to the purpose of improving the resolution of seismic data to identify thin-layer reflections. Therefore, how to improve the resolution of seismic data and reduce the suppression of weak reflection information becomes an important problem. In addition, the technology is based on a single-pass deconvolution method, does not consider the spatial relationship of reservoir structures, and causes discontinuous processing results in the transverse direction.
In order to alleviate the problems, the invention provides a method for improving the seismic characterization precision of a thin-layer structure. The statistical probability distribution function represents the real distribution of the reflection coefficient of the work area, so that the prediction precision of the reflection coefficient is improved, and the influence of human factors on the inversion result is reduced. And then, introducing dip angle information of the seismic data into an inverted objective function, establishing a multi-channel nonlinear deconvolution of dip angle constraint, and ensuring the transverse continuity of a processing result while improving the seismic resolution.
Therefore, based on the problems, the method and the device for improving the thin-layer structure seismic representation precision are provided, the method and the device are suitable for seismic prediction and description of complex reservoirs, thin reservoirs can be reflected more effectively, inversion results are closer to actual geological features, the defect of constraint sparse deconvolution can be well made up, the method and the device for improving the thin-layer structure seismic representation precision are used for accurately describing reservoir features, and the method and the device have important practical significance.
Disclosure of Invention
The invention provides a method and a device for improving the seismic representation precision of a thin-layer structure, which are suitable for seismic prediction and description of complex reservoirs, can more effectively reflect thin reservoirs, can make up the defect of constraint sparse deconvolution, and can realize fine description of reservoir characteristics, wherein the inversion result is closer to actual geological characteristics.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the method for improving the seismic characterization precision of the thin-layer structure comprises the following steps:
acquiring original seismic data; establishing a multi-channel deconvolution model according to the original seismic data:
d＝Gm
wherein d = [ s ] 1 ，s 2 ，…，s N ]Representing observed seismic data consisting of multiple seismic channel observed data connected end to end in sequence, d i Observation data representing an ith seismic trace; g is a block diagonal matrix, and elements on the diagonal of the block diagonal matrix are convolution matrixes of seismic wavelets of multiple seismic traces respectively; m = [ r ] 1 ，r 2 ，…，r N ]Representing multi-channel model parameters formed by connecting multi-seismic-channel model parameters end to end in sequence, r i Representing a sequence of reflection coefficients of an ith seismic trace; n is the total number of seismic channels;
establishing a multi-channel deconvolution target function:
obtaining prior probability distribution function P of reflection coefficient w (m) applying a probability distribution constraint to the reflection coefficients in the longitudinal direction such that the parametric distribution function P est (m) probability distribution function P of reflection coefficient of the region w (m) are consistent;
wherein the probability distribution constraint is:
P est (m) is the probability distribution of the multi-channel model parameter m; is obtained by carrying out probability statistics on m;
acquiring dip angle information, and constructing a spatial constraint item H by using the dip angle information, wherein the constraint item comprises the dip angle information of a same phase axis in the whole seismic channel;
constructing a multi-channel deconvolution target functional based on the inclination angle:
wherein, mu z Weight factor, mu, representing a time-wise constraint term governing the seismic trace x Adjusting the proportion of the inclination angle constraint term in the objective function;
and solving the multi-channel deconvolution target functional to obtain seismic record data with improved resolution.
Further, the method for obtaining the prior probability distribution function of the reflection coefficient comprises:
selecting a reference well, extracting a probability distribution histogram of the reflection coefficient according to the logging data of the reference well to obtain a prior probability distribution function p w (x) The reflection coefficient distribution of the target area is characterized by the function.
Further, the logging data of the reference well can reflect the distribution characteristics of the reflection coefficient of the target area.
Further, the method for acquiring the inclination angle information comprises the following steps:
carrying out dip angle scanning on the seismic section to obtain dip angle information theta (x, t) of the seismic data;
recording the cross-correlation coefficient in the dip angle scanning as the confidence coefficient c (x, t) of the dip angle, and setting the threshold value c 0 When the confidence c (x, t) < c 0 Then, the dip angle information is obtained by fitting the surrounding dip angles;
wherein, the first and the second end of the pipe are connected with each other,
w is a weight coefficient matrix, the size of which is related to the distance between the dip angle sampling point to be solved and the surrounding sampling points, and w (0, 0) =0; t is a time sequence, it is an index number of the time sequence, x is a seismic channel sequence, ix is a seismic channel index number, and a and b are index variables.
Utilize logging curve and petrology characteristic to divide device of weathered shell structure, including the following module:
the multi-channel deconvolution target function establishing module is used for acquiring original seismic data; establishing a multi-channel deconvolution model according to the original seismic data:
d＝Gm
wherein d = [ s ] 1 ，s 2 ，…，s N ]Representing observed seismic data consisting of multiple seismic channel observed data connected end to end in sequence, d i Observation data representing an ith seismic trace; g is a block diagonal matrix, and elements on the diagonal of the block diagonal matrix are convolution matrixes of seismic wavelets of multiple seismic traces respectively; m = [ r ] 1 ，r 2 ，…，r N ]Representing multi-channel model parameters formed by connecting multi-seismic-channel model parameters end to end in sequence, r i Representing a sequence of reflection coefficients of an ith seismic trace; n is the total number of seismic channels;
establishing a multi-channel deconvolution target function:
obtaining prior probability distribution function P of reflection coefficient w (m) applying a probability distribution constraint to the reflection coefficients in the longitudinal direction such that the parametric distribution function P est (m) probability distribution function P of reflection coefficient of the area w (m) are consistent;
wherein the probability distribution constraint is:
P est (m) is the probability distribution of the multi-channel model parameter m; is obtained by carrying out probability statistics on m;
the spatial constraint item establishing module is used for acquiring dip angle information and establishing a spatial constraint item H by using the dip angle information, wherein the constraint item comprises the dip angle information of a same phase axis in the whole seismic channel;
the multi-channel deconvolution target functional building module is used for building a multi-channel deconvolution target functional based on the inclination angle:
wherein, mu z Weight factor, mu, representing a time-wise constraint term governing the seismic trace x Adjusting the proportion of the inclination angle constraint term in the objective function;
and the multi-channel deconvolution target functional solving module is used for solving the multi-channel deconvolution target functional to obtain the seismic record data with improved resolution.
A computing device, comprising: one or more processing units; a storage unit for storing one or more programs, wherein when the one or more programs are executed by the one or more processing units, the one or more processing units are caused to perform the method for improving the seismic characterization accuracy of a thin-layer structure as described above.
A computer readable storage medium having non-volatile program code executable by a processor, the computer program when executed by the processor implementing the steps of the method of improving the seismic characterization accuracy of a thin-layer structure as described above.
The invention has the advantages and positive effects that:
extracting a probability distribution function of a reflection coefficient of a target area according to logging data, and longitudinally constraining the probability distribution function; constructing transverse constraint by using dip angle information in seismic data, and improving the reliability and stability of a multi-channel deconvolution target functional; the method has the advantages that the problems of transverse instability and discontinuity of the traditional single-channel inversion are solved, longitudinal constraint is carried out through the probability distribution of the statistical reflection coefficient, the influence of mathematical distribution such as sparse constraint on the inversion result is solved, the accuracy of the high-resolution inversion result is improved, the interpretability of seismic data is enhanced, and the method has strong implementability.
Drawings
The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and examples, but it should be understood that these drawings are designed for illustrative purposes only and thus do not limit the scope of the present invention. Furthermore, unless otherwise indicated, the drawings are intended to be illustrative of the structural configurations described herein only, and are not necessarily drawn to scale.
FIG. 1 is a flow chart of an embodiment of the method for improving seismic characterization accuracy of a thin-layer structure;
FIG. 2 is a seismic record containing noise in example 1 of the present invention;
FIG. 3 is a histogram of probability distribution of reflection coefficients according to well data statistics in example 1 of the present invention;
FIG. 4 is a dip field of the seismic record of FIG. 2 in example 1 of the present invention;
FIG. 5 (a) shows the result of using conventional sparse deconvolution processing in example 1 of the present invention;
FIG. 5 (b) shows the results of the treatment according to the present method in example 1 of the present invention;
FIG. 6 (a) is the actual seismic data in example 2 of the present invention;
FIG. 6 (b) is a seismic record of a conventional sparse deconvolution process in example 2 of the present invention;
FIG. 6 (c) is a seismic record processed by the method of example 2 of the present invention;
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any single feature described or implicit in the embodiments described herein or shown or implicit in the drawings may continue to be combined or subtracted from any single feature or equivalent thereof to obtain still further embodiments of the invention that may not be directly mentioned herein. In addition, for the sake of simplicity, the same or similar features may be indicated in only one place in the same drawing.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on orientations or positional relationships shown in the drawings or orientations or positional relationships that the present product is conventionally placed in use, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1
As shown in fig. 1, the method for improving seismic characterization accuracy of a thin-layer structure provided by this embodiment includes the following steps:
the single pass convolution model can be expressed as:
s＝Wr
wherein s represents single-channel seismic data, W represents a convolution matrix form of seismic wavelets, and r represents a reflection coefficient sequence corresponding to the seismic channel;
based on the single-pass convolution model described by the above formula, the following multi-pass forward model can be established:
d＝Gm
wherein d = [ s ] 1 ，s 2 ，…，s N ]Representing observed seismic data consisting of multiple seismic channel observed data connected end to end in sequence, d i Observation data representing an ith seismic trace; g is a block diagonal matrix, and elements on the diagonal of the block diagonal matrix are convolution matrixes W of seismic wavelets of multiple seismic traces respectively; m = [ r ] 1 ，r 2 ，…，r N ]Representing a plurality of seismic trace model parameters formed by connecting the model parameters of a plurality of seismic traces end to end in sequence, r i Representing the sequence of reflection coefficients for the ith seismic trace. N represents the total number of seismic traces and is 350. The following multi-pass deconvolution objective function can be built according to the above equation:
step 3, counting the prior probability distribution function of the reflection coefficient:
selecting a well with better quality as a reference well, wherein the logging data of the reference well can reflect the distribution characteristics of the reflection coefficient of a target area, extracting a probability distribution histogram (figure 3) of the reflection coefficient according to reliable well data, wherein the histogram describes the distribution characteristics of the reflection coefficient of the area, and obtaining the reflection coefficient r i Probability distribution p of time w (r i )。
Obtaining prior probability distribution function P of reflection coefficient w (m) applying a probability distribution constraint to the reflection coefficients in the longitudinal direction such that the parametric distribution function P est (m) probability distribution function P of reflection coefficient of the area w (m) are consistent;
wherein the probability distribution constraint is:
P est (m) is the probability distribution of the multi-channel model parameter m; the method is obtained by carrying out probability statistics on m;
step 4, obtaining inclination angle information:
dip angle scanning is carried out on the seismic section to obtain dip angle distribution characteristics theta (x, t) of seismic data, meanwhile, in order to evaluate the accuracy of dip angle scanning, cross-correlation coefficients in dip angle scanning are recorded as confidence coefficients c (x, t) of dip angles, and a threshold c is set 0 =0.6, when the confidence c (x, t) < c 0 Then, the estimation accuracy of the dip angle is not high, and the dip angle should be obtained by fitting the surrounding dip angle values:
w is a weight coefficient matrix, the size of the weight coefficient matrix is related to the distance between the dip angle sampling point to be solved and the surrounding sampling points, and w (0, 0) =0; the resulting tilt angle field is shown in fig. 4.
Step 5, constructing a space constraint item H by using the dip angle information, wherein the constraint item comprises the dip angle information of the same phase axis in the whole seismic channel;
and 6, constructing a multi-channel deconvolution target functional based on the dip angle constraint based on the steps:
μ z weight factor, mu, representing a time-wise constraint term governing the seismic trace x Adjusting the weight of the tilt constraint term, in this example, μ z ＝0.3，μ x ＝0.2；
Step 7, solving the objective function of the step 6, and outputting the seismic record with improved resolution, as shown in fig. 5 (b); the conventional single-pass deconvolution result is shown in fig. 5 (a), and the comparison shows that the seismic section processed by the method recovers more weak reflection and the in-phase axis is more continuous.
Example 2
In this embodiment, a certain work area of the eastern oilfield is taken as an example to test the feasibility and effectiveness of the invention. The original post-stack seismic profile is shown in fig. 6 (a), and comprises 220 seismic traces, each trace has 402 sampling points, and the sampling interval is 2ms; the traditional single pass sparse deconvolution and the processing effect of the present invention are shown in fig. 6 (b) and 6 (c), respectively; it can be seen that the single-channel sparse deconvolution suppresses weak reflection in the original seismic record, is influenced by noise, has poor transverse continuity of inversion in the event of the same phase, and is not easy to further process and explain; the method recovers more weak reflection information, maintains the spatial structure relationship of the same phase axis in the original data, and more effectively reflects the thin layer structure.
Example 3
Utilize logging curve and petrology characteristic to divide device of weathered shell structure, including the following module:
the multi-channel deconvolution target function establishing module is used for acquiring original seismic data; establishing a multi-channel deconvolution model according to the original seismic data:
d＝Gm
wherein d = [ s ] 1 ，s 2 ，…，s N ]Representing observed seismic data consisting of multiple seismic channel observed data connected end to end in sequence, d i Representing observation data of an ith seismic trace; g is a block diagonal matrix, and elements on the diagonal of the block diagonal matrix are convolution matrixes of seismic wavelets of multiple seismic traces respectively; m = [ r ] 1 ，r 2 ，…，r N ]Representing a plurality of seismic trace model parameters formed by connecting the model parameters of a plurality of seismic traces end to end in sequence, r i Representing a sequence of reflection coefficients of an ith seismic trace; n is the total number of seismic channels;
establishing a multi-channel deconvolution target function:
obtaining prior probability distribution function P of reflection coefficient w (m) applying a probability distribution constraint to the reflection coefficients in the longitudinal direction such that the parametric distribution function P est (m) probability distribution function P of reflection coefficient of the area w (m) are consistent;
wherein the probability distribution constraint is:
P est (m) is the probability distribution of the multi-channel model parameter m; is obtained by carrying out probability statistics on m;
the spatial constraint item establishing module is used for acquiring dip angle information and establishing a spatial constraint item H by using the dip angle information, wherein the constraint item comprises the dip angle information of a same phase axis in the whole seismic channel;
the multi-channel deconvolution target functional establishing module is used for establishing a multi-channel deconvolution target functional based on the inclination angle:
wherein, mu z To indicate controlWeight factor, mu, of time-direction constraint term in seismic channel x Adjusting the proportion of the inclination angle constraint term in the objective function;
and the multi-channel deconvolution target functional solving module is used for solving the multi-channel deconvolution target functional to obtain the seismic record data with improved resolution.
A computing device, comprising:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the method for compartmentalizing a weathered shell structure using logs and petrophysical features as described above; it is noted that the computing device may include, but is not limited to, a processing unit, a storage unit; those skilled in the art will appreciate that the computing device includes processing units, memory units, and not limitation of the computing device, and may include more components, or combine certain components, or different components, e.g., the computing device may also include input output devices, network access devices, buses, and the like.
A computer readable storage medium having non-transitory program code executable by a processor, the computer program when executed by the processor implementing the steps of the above method for compartmentalizing a weathered shell structure using well logs and petrophysical features; it should be noted that the readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof; the program embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. For example, program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, or entirely on a remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
The present invention has been described in detail with reference to the above examples, but the description is only for the preferred examples of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (7)
1. The method for improving the seismic characterization precision of the thin-layer structure is characterized by comprising the following steps: the method comprises the following steps:
acquiring original seismic data; establishing a multi-channel deconvolution model according to the original seismic data:
d＝Gm
wherein d = [ s ] 1 ,s 2 ,…,s N ]Representing observed seismic data consisting of multiple seismic channel observed data connected end to end in sequence, d i Observation data representing an ith seismic trace; g is a block diagonal matrix, and elements on the diagonal of the block diagonal matrix are convolution matrixes of seismic wavelets of multiple seismic traces respectively; m = [ r ] 1 ,r 2 ,…,r N ]Representing a plurality of seismic trace model parameters formed by connecting the model parameters of a plurality of seismic traces end to end in sequence, r i Representing a sequence of reflection coefficients of an ith seismic trace; n is the total number of seismic channels;
establishing a multi-channel deconvolution target function:
obtaining prior probability distribution function P of reflection coefficient w (m) coefficient of retroreflection in the longitudinal directionApplying probability distribution constraint to make parameter distribution function P est (m) probability distribution function P of reflection coefficient of the area w (m) are consistent;
wherein the probability distribution constraint is:
P est (m) is the probability distribution of the multi-channel model parameter m;
acquiring dip angle information, and constructing a spatial constraint item H by using the dip angle information, wherein the constraint item comprises dip angle information of a same phase axis in the whole seismic channel;
constructing a multi-channel deconvolution target functional based on the inclination angle:
wherein, mu z Weight factor, mu, representing a time-wise constraint term governing the seismic trace x Adjusting the proportion of the inclination angle constraint term in the objective function;
and solving the multi-channel deconvolution target functional to obtain seismic record data with improved resolution.
2. The method of improving seismic characterization accuracy of a thin-layer structure according to claim 1, wherein: the method for obtaining the prior probability distribution function of the reflection coefficient comprises the following steps:
selecting a reference well, extracting a probability distribution histogram of the reflection coefficient according to the logging data of the reference well to obtain a prior probability distribution function p w (x) The reflection coefficient distribution of the target area is characterized by the function.
3. The method of using well logs and petrophysical features to classify a weathered shell structure according to claim 2, wherein: the logging data of the reference well can embody the distribution characteristics of the reflection coefficient of the target area.
4. The method for compartmentalizing a weathered shell structure using logs and petrological features as recited in claim 3, wherein: the method for acquiring the inclination angle information comprises the following steps:
carrying out dip angle scanning on the seismic section to obtain dip angle information theta (x, t) of seismic data;
recording the cross correlation coefficient in the dip angle scanning as the confidence coefficient c (x, t) of the dip angle, and setting the threshold value c 0 When the confidence c (x, t) < c 0 Then, the inclination angle information is obtained by fitting the surrounding inclination angles;
wherein the content of the first and second substances,
w is a weight coefficient matrix, the size of which is related to the distance between the dip angle sampling point to be solved and the surrounding sampling points, and w (0, 0) =0; t is a time sequence, it is an index number of the time sequence, x is a seismic channel sequence, ix is a seismic channel index number, and a and b are index variables.
5. Utilize logging curve and petrology characteristic to divide device of weathered shell structure, its characterized in that: the system comprises the following modules:
the multi-channel deconvolution target function establishing module is used for acquiring original seismic data; establishing a multi-channel deconvolution model according to the original seismic data:
d＝Gm
wherein d = [ s ] 1 ,s 2 ,…,s N ]Representing observed seismic data consisting of multiple seismic channel observed data connected end to end in sequence, d i Observation data representing an ith seismic trace; g is a block diagonal matrix, and elements on the diagonal of the block diagonal matrix are convolution matrixes of seismic wavelets of multiple seismic traces respectively; m = [ r ] 1 ,r 2 ,…,r N ]Representing a plurality of seismic trace model parameters formed by connecting the model parameters of a plurality of seismic traces end to end in sequence, r i Representing a sequence of reflection coefficients of an ith seismic trace; n is the total number of seismic channels;
establishing a multi-channel deconvolution target function:
obtaining a prior probability distribution function P of the reflection coefficient w (m) applying a probability distribution constraint to the reflection coefficients in the longitudinal direction such that the parametric distribution function P est (m) probability distribution function P of reflection coefficient of the area w (m) are consistent;
wherein the probability distribution constraint is:
P est (m) is the probability distribution of the multi-channel model parameter m;
the spatial constraint item establishing module is used for acquiring dip angle information and establishing a spatial constraint item H by using the dip angle information, wherein the constraint item comprises the dip angle information of a same phase axis in the whole seismic channel;
the multi-channel deconvolution target functional establishing module is used for establishing a multi-channel deconvolution target functional based on the inclination angle:
wherein, mu z Weight factor, mu, representing a time-wise constraint term governing the seismic trace x Adjusting the proportion of the inclination angle constraint term in the objective function;
and the multi-channel deconvolution target functional solving module is used for solving the multi-channel deconvolution target functional to obtain seismic record data with improved resolution.
6. A computing device, characterized by: the method comprises the following steps: one or more processing units; a storage unit to store one or more programs that, when executed by the one or more processing units, cause the one or more processing units to perform the method of any of claims 1-4.
7. A computer-readable storage medium with non-volatile program code executable by a processor, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 4 when executed by the processor.
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| CN117741750B (en) * | 2024-02-21 | 2024-04-26 | 东北石油大学三亚海洋油气研究院 | Multi-channel pre-stack deconvolution method and system based on Radon transformation |
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