CN117310818A - Post-stack seismic data processing method and device based on image-guided three-dimensional filtering - Google Patents
Post-stack seismic data processing method and device based on image-guided three-dimensional filtering Download PDFInfo
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Abstract
The application provides a post-stack seismic data processing method and device based on image-guided three-dimensional filtering, wherein the method comprises the following steps: acquiring original post-stack seismic data, guide data and regularization factors; based on the guide data and the regularization factor, establishing a linear weighted average filter and a filtering kernel function thereof; determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm; calculating to obtain a kernel function based on image-guided three-dimensional filtering through the related parameters and the filtering kernel function; and filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the filtered post-stack seismic data. By utilizing a kernel function based on image-guided three-dimensional filtering, noise is compressed in a self-adaptive mode, trend and inclination information of an extracted horizon do not need to be displayed, key information of earthquake fracture and cracks is effectively reserved, and the signal to noise ratio of an earthquake image is improved.
Description
Technical Field
The invention relates to the technical field of seismic data processing, in particular to a post-stack seismic data processing method and device based on image guided three-dimensional filtering.
Background
In seismic exploration, structural features such as faults and cracks are analyzed and described by utilizing seismic data, and the method has important significance for finding and describing oil reservoirs, can help to decide an optimal development scheme, reduces the well exploration risk and improves the success rate of petroleum exploration. However, the geological structure of the seismic exploration area is generally complex, the stratum morphology of the fracture zone is changeable, so that the acquired seismic data often has the problem of low signal-to-noise ratio, and the seismic data processing and interpretation become more difficult. High-quality seismic data is an important basis and guarantee for high-quality interpretation of seismic data and accurate prediction of oil and gas reservoirs. Therefore, efficient denoising techniques are needed to improve the quality and reliability of seismic data.
Although seismic data processing and interpretation personnel explore a series of seismic data filtering technologies at the present stage to improve the signal to noise ratio of the seismic data, and further improve the accuracy of seismic data interpretation and hydrocarbon reservoir prediction. However, compared with a general image processing algorithm, the method can suppress the amplitude of an inclined fault, destroy structures lacking continuity such as small faults and cracks and the like and mismatch the same-phase axes on two sides of a large fault in the processing process of the seismic data.
Disclosure of Invention
The invention provides a post-stack seismic data processing method and device based on image-guided three-dimensional filtering, which are used for rapidly and effectively carrying out edge-preserving structural smoothing on a seismic image on the premise of preserving seismic structural information by introducing a guided image filtering technology and utilizing a noisy seismic image as a constraint condition.
In a first aspect, the present invention provides a post-stack seismic data processing method based on image-guided three-dimensional filtering, including:
acquiring original post-stack seismic data, guide data and regularization factors;
based on the guide data and the regularization factor, establishing a linear weighted average filter and a filtering kernel function thereof;
determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm;
calculating to obtain a kernel function based on image-guided three-dimensional filtering through the related parameters and the filtering kernel function;
and filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the filtered post-stack seismic data.
Optionally, based on the guiding data and the regularization factor, establishing a linear weighted average filter and a filtering kernel function thereof includes:
defining the linear weighted average filter using pilot data;
and under the framework of the linear weighted average filter, combining the regularization factors, adjusting the weight coefficient of the linear weighted average filter, and introducing the filtering kernel function of an explicit bilateral filter after adjustment.
Optionally, filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering, and after obtaining the post-stack seismic data after filtering, further including:
and analyzing the filtered post-stack seismic data, including observing spectral characteristics, amplitude changes and spatial distribution, and carrying out evaluation and analysis according to analysis results to obtain corresponding underground structure and seismic event information.
Optionally, the filtering kernel function is:
the expression of the linear weighted average filter is as follows:
wherein W is i，j (G) For a filter kernel with respect to G, G is the pilot data,is window w k The number of data points in the image, k is the pixel position in the image, G i For a pixel value in the pilot data at the ith point, G j For a pixel value, m, in the pilot data at the ith point k And->Respectively window w k Mean and variance of the internal data G, E is regularization factor, < >>For the input data at the j-th point, < +.>Is the output data at the i-th point.
In a second aspect, the present invention provides a post-stack seismic data processing device based on image-guided three-dimensional filtering, comprising:
the acquisition module is used for acquiring the original post-stack seismic data, the guide data and the regularization factors;
the establishing module is used for establishing a linear weighted average filter and a filtering kernel function thereof based on the guide data and the regularization factor;
the parameter determining module is used for determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm;
the kernel function calculation module is used for calculating a kernel function based on image-guided three-dimensional filtering through the related parameters and the filtering kernel function;
and the filtering module is used for filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the post-stack seismic data after filtering.
Optionally, the establishing module includes:
a filter definition sub-module for defining the linear weighted average filter using pilot data;
and the adjustment sub-module is used for adjusting the weight coefficient of the linear weighted average filter by combining the regularization factor under the framework of the linear weighted average filter, and introducing the filtering kernel function of the explicit bilateral filter after adjustment.
Optionally, the method further comprises:
and the analysis module is used for analyzing the filtered post-stack seismic data, including observation of spectral characteristics, amplitude variation and spatial distribution, and carrying out evaluation and analysis according to analysis results to obtain corresponding underground structure and seismic event information.
Optionally, the filtering kernel function is:
the expression of the linear weighted average filter is as follows:
wherein W is i，j (G) For a filter kernel with respect to G, G is the pilot data,is window w k The number of data points in the image, k is the pixel position in the image, G i For a pixel value in the pilot data at the ith point, G j For a pixel value, m, in the pilot data at the ith point k And->Respectively window w k Mean and variance of the internal data G, E is regularization factor, < >>For the input data at the j-th point, < +.>Is the output data at the i-th point.
In a third aspect, the present application provides an electronic device comprising a processor and a memory storing computer readable instructions which, when executed by the processor, perform the steps of the method as provided in the first aspect above.
In a fourth aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method as provided in the first aspect above.
From the above technical scheme, the invention has the following advantages:
the invention provides a post-stack seismic data processing method and device based on image-guided three-dimensional filtering, wherein the method comprises the following steps: acquiring original post-stack seismic data, guide data and regularization factors; based on the guide data and the regularization factor, establishing a linear weighted average filter and a filtering kernel function thereof; determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm; calculating to obtain a kernel function based on image-guided three-dimensional filtering through the related parameters and the filtering kernel function; and filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the filtered post-stack seismic data. By utilizing a kernel function based on image-guided three-dimensional filtering, noise is compressed in a self-adaptive mode, trend and inclination information of an extracted horizon do not need to be displayed, key information of earthquake fracture and cracks is effectively reserved, and the signal to noise ratio of an earthquake image is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flowchart illustrating a method for processing post-stack seismic data based on image-guided three-dimensional filtering according to one embodiment of the present invention;
FIG. 2 is a flowchart illustrating a second embodiment of a post-stack seismic data processing method based on image-guided three-dimensional filtering according to the present invention;
FIG. 3 is a schematic representation of a noisy image;
FIG. 4 is a schematic diagram of guided image filtering;
FIG. 5 is a median filtering schematic;
FIG. 6 is a schematic diagram of mean filtering;
FIG. 7 is a schematic diagram of Gaussian filtering;
FIG. 8 is a pre-noise image of a multi-boundary two-dimensional block image denoising test;
FIG. 9 is a denoised image of a multi-boundary two-dimensional block image denoising test;
FIG. 10 is a guided image filtering result image of a multi-boundary two-dimensional block image denoising test;
FIG. 11 is a median filtering result image of a multi-boundary two-dimensional block image denoising test;
FIG. 12 is a mean value filtering result image of a multi-boundary two-dimensional block image denoising test;
FIG. 13 is a Gaussian filtered result image of a multi-boundary two-dimensional block image denoising test;
FIG. 14 is a schematic view of raw post-stack seismic data;
FIG. 15 is a schematic view of filtered seismic data;
FIG. 16 is a schematic diagram of coherence properties of raw post-stack seismic data;
FIG. 17 is a schematic diagram of coherence properties of filtered seismic data;
FIG. 18 is a block diagram illustrating an embodiment of a post-stack seismic data processing device based on image-guided three-dimensional filtering in accordance with the present invention.
Detailed Description
The embodiment of the invention provides a post-stack seismic data processing method and device based on image-guided three-dimensional filtering, which are used for rapidly and effectively carrying out edge-preserving structural smoothing on a seismic image on the premise of preserving seismic structural information by introducing a guided image filtering technology and utilizing a noisy seismic image as a constraint condition.
In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for processing post-stack seismic data based on image-guided three-dimensional filtering according to an embodiment of the present invention, including:
s101, acquiring original post-stack seismic data, guide data and regularization factors;
s102, based on the guide data and the regularization factors, establishing a linear weighted average filter and a filtering kernel function thereof;
s103, determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm;
s104, calculating a kernel function based on image-guided three-dimensional filtering through the relevant parameters and the filtering kernel function;
s105, filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the filtered post-stack seismic data.
The post-stack seismic data processing method based on image-guided three-dimensional filtering provided by the embodiment of the invention comprises the following steps: acquiring original post-stack seismic data, guide data and regularization factors; based on the guide data and the regularization factor, establishing a linear weighted average filter and a filtering kernel function thereof; determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm; calculating to obtain a kernel function based on image-guided three-dimensional filtering through the related parameters and the filtering kernel function; and filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the filtered post-stack seismic data. By utilizing a kernel function based on image-guided three-dimensional filtering, noise is compressed in a self-adaptive mode, trend and inclination information of an extracted horizon do not need to be displayed, key information of earthquake fracture and cracks is effectively reserved, and the signal to noise ratio of an earthquake image is improved.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for processing post-stack seismic data based on image-guided three-dimensional filtering according to a second embodiment of the present invention, where the method includes:
s201, acquiring original post-stack seismic data, guide data and regularization factors;
it should be noted that the original post-stack seismic data, i.e., data obtained from a seismic survey, contains information about the subsurface structure.
The pilot data, i.e., one or more auxiliary images, is used to guide the processing of the seismic data. The guidance data may be high quality seismic data, geophysical attribute data, or other related images.
Half window width, i.e., half the width of the window used in the filtering process. The window is used to define the neighborhood range of the filter.
Regularization factors, i.e., adjustment parameters used to balance weights between the pilot data and the original seismic data.
S202, defining the linear weighted average filter by using guide data;
in the embodiment of the invention, a linear weighted average filter of a filtering kernel function is defined by referring to the design thought of an explicit weighted average filter, wherein the expression of the linear weighted average filter is as follows:
wherein W is i，j (G) As a filter kernel for G, G is the pilot data (which may be the input data itself),for the input data at the j-th point (input data independent of the filter kernel)>Is the output data at the i-th point.
S203, under the framework of the linear weighted average filter, combining the regularization factors, adjusting the weight coefficient of the linear weighted average filter, and introducing the filtering kernel function of an explicit bilateral filter after adjustment;
in an embodiment of the invention, a linear weighted average filter is first defined using the pilot data. The weight coefficients in the filter are then adjusted in combination with the regularization factor to balance the importance between the pilot data and the original data. Finally, in the linear weighted average filter, a filter kernel function of an explicit bilateral filter is introduced to take into account differences in spatial distance and gray values between pixels.
In a specific implementation, in the framework of the linear weighted average filter, the filtering kernel function of the explicit bilateral filter is:
wherein,x is the spatial coordinates at the data point, K i For regularization operator, σ s Sum sigma r The spatial and amplitude ranges are controlled separately.
Meanwhile, in the embodiment of the invention, under the framework of displaying the bilateral filter, the output data and the guide data have the same image edge, so that the gradient of the output data and the guide data is ensured to be linearly related, namely:
wherein,gradient for output data，/>For guiding the gradient of the data, a is the weight for adjusting the gradient of the guiding image.
In addition, since the guidance data and the output data have a linear relationship in a local range, the relationship between the guidance data and the output data in each window during the window data processing can be expressed as follows:
wherein k is window w k Center coordinates of (a) k ，b k ) Is a parameter characterizing the linear relationship, w k Is a constant time window, the half window width is r, G i One pixel value in the data is directed at the i-th point.
S204, determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm;
s205, calculating a kernel function based on image-guided three-dimensional filtering through the relevant parameters and the filtering kernel function;
in the embodiment of the present invention, assuming that the difference between the input data and the output data is random noise, the parameter (a) is found in each window k ，b k ) Is:
the expression of the relevant parameters that can explicitly solve the linear relationship within the window using the linear regression algorithm is:
wherein,is window w k The number of data points in the image, k is the pixel position in the image, G j For a pixel value, m, in the pilot data at the ith point k And->Respectively window w k The mean and variance of the internal data G, e is the regularization factor.
Under the windowing strategy, since the partial output data overlapped at the windows has a non-unique value, the average output of overlapped windows at the same point is:
wherein,
thereby further deriving a kernel function based on image-guided three-dimensional filtering:
it should be noted that the larger half window width and regularization factor can generate stronger smoothing effect, possibly damage the fracture structure, and in actual calculation, proper parameters need to be determined according to comprehensive consideration of requirements of edge protection and smoothness.
S206, filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain filtered post-stack seismic data;
s207, analyzing the filtered post-stack seismic data, including observing spectral characteristics, amplitude changes and spatial distribution, and carrying out evaluation and analysis according to analysis results to obtain corresponding underground structure and seismic event information.
In order to verify the correctness of the post-stack seismic data processing method based on image-guided three-dimensional filtering and determine the advantages of the method compared with a post-stack seismic data processing method using a conventional filter, a simple two-dimensional block model image with more boundaries is designed, and then a certain random noise signal is added to obtain a noise-containing image shown in fig. 3, so that the effect of various filtering algorithms is evaluated later. In contrast, the image data containing noise is respectively subjected to guided image filtering, median filtering, mean filtering and gaussian filtering, the same half window width is selected in calculation, and the final results are respectively shown in fig. 4 to 7, wherein fig. 4 is a schematic diagram of guided image filtering (i.e., image-based guided three-dimensional filtering in the embodiment of the present invention), fig. 5 is a schematic diagram of median filtering, fig. 6 is a schematic diagram of mean filtering, and fig. 7 is a schematic diagram of gaussian filtering. According to the filtering result, it can be seen that partial random noise is effectively eliminated by the methods of leading image filtering, median filtering, mean filtering and Gaussian filtering. The guided image filtering and median filtering method has a good protection effect on the layering boundary, and the average filtering and Gaussian filtering blur the boundary while denoising. The guided image filtering method has a good protection against sharp corner boundaries in the image like scattering points (e.g. at x=50, y=100) compared to the median filtering method, which blurs such structures.
To more clearly show the above phenomenon, the values at the diagonal lines of the images in fig. 3 to 7 are shown in more detail in fig. 8 to 13, wherein fig. 8 is a pre-noise image of the multi-border two-dimensional block image denoising test, fig. 9 is a post-noise image of the multi-border two-dimensional block image denoising test, fig. 10 is a guide image filtering result image of the multi-border two-dimensional block image denoising test, fig. 11 is a median filtering result image of the multi-border two-dimensional block image denoising test, fig. 12 is a mean filtering result image of the multi-border two-dimensional block image denoising test, fig. 13 is a gaussian filtering result image of the multi-border two-dimensional block image denoising test, and it can be seen that in fig. 11, the median filtering result image produces an unrealistic structure by carefully comparing the boundaries of pixel point numbers 100 in fig. 8 to 13; in fig. 12 and 13, the average value filtering result image and the gaussian filtering result image excessively blur the boundary, and only the guide image filtering result image of fig. 10 can well store the structural information. In addition to guiding the image filtering result image, the performance of other filters is obviously disadvantageous to the tiny fracture structural formula common in practical materials. In contrast, the image-guided three-dimensional filtering-based method provided by the embodiment of the invention can well keep structural information under the self-guiding condition while inhibiting random noise, is simpler in processing, and can more conveniently and reliably explain fracture on the basis of the structural information.
The post-stack seismic data processing method based on image guided three-dimensional filtering provided by the embodiment of the invention is creatively expanded to three-dimensional seismic image filtering based on image guided two-dimensional filtering, a linear weighted average filter and an explicit bilateral filter kernel function related to a guided image are sequentially constructed, a linear correlation function of output data and guided data is established, a linear correlation coefficient is obtained by utilizing linear regression through time-sharing window control, and finally the three-dimensional filtering kernel function based on image guidance is obtained. By utilizing a kernel function based on image-guided three-dimensional filtering, noise is compressed in a self-adaptive mode, trend and inclination information of an extracted horizon do not need to be displayed, key information of earthquake fracture and cracks is effectively reserved, and the signal to noise ratio of an earthquake image is improved. And after further calculating the coherence attribute, the fracture structure feature image of the small micro fracture area is found to be more obvious, and the description is clearer, so that the subsequent earthquake analysis work is facilitated.
The filtered post-stack seismic data resulting from the processing of the present invention is illustrated below to facilitate an understanding of the benefits of the present invention by those skilled in the art.
Examples: the post-stack seismic data processing method based on image guided three-dimensional filtering is applied to the original post-stack seismic data shown in fig. 14, the data contains a fault structure and has low signal to noise ratio, the original data is taken as guiding data, guided image filtering processing is carried out, the filtered seismic data shown in fig. 15 is obtained, and the signal to noise ratio of a seismic migration section is obviously improved in the filtered seismic data. Besides the large fault features being more obvious, the small fracture details of the fracture structure at the lower right corner of the lower right section are more clear, and the continuity of the non-fracture structure seismic event is obviously improved, so that the interpretation of faults and horizons by interpretation staff is facilitated.
On this, the coherence attribute information is extracted for the seismic data before and after the guided image filtering processing by using the eigenvalue method, and the results are shown in the coherence attribute diagram of the original post-stack seismic data in fig. 16 and the coherence attribute diagram of the filtered seismic data in fig. 17. By observing the coherence attribute image of the original post-stack seismic data, it can be seen that the background noise at the position near the more complex fracture position, such as the position of the dashed circle in fig. 16, has a significant influence, so that the resolution of the coherence processing on faults is greatly reduced, and the subsequent interpretation work is difficult.
In the example, because the signal-to-noise ratio of the fracture zone seismic image is low, the noise interference is large, the continuity of the same phase axis is not obvious enough, and the coherence calculated by the traditional method is difficult to distinguish between the seismic horizon and the fracture structure; in fig. 17, the filtered seismic data effectively suppresses noise existing in the seismic section without destroying the original information of the same phase axis, so as to generate coherent slices with higher signal-to-noise ratio as a whole, the fracture layers of the complex fracture structure are clearer, the hierarchical relationship among the multi-stage fracture is clearer, and especially the broken line position of fig. 17 better highlights the fine fracture. It is easy to see that after the guided image filtering without explicit calculation of fault dip angle and trend is acted on the noisy three-dimensional seismic data, the quality of the corresponding coherent slice is obviously improved, and good guiding value is provided for subsequent seismic interpretation work.
Referring to fig. 18, fig. 18 is a block diagram illustrating an embodiment of a post-stack seismic data processing device based on image guided three-dimensional filtering according to the present invention, including:
an acquisition module 301, configured to acquire original post-stack seismic data, guide data, and regularization factors;
a building module 302, configured to build a linear weighted average filter and a filtering kernel function thereof based on the guiding data and the regularization factor;
a parameter determining module 303, configured to determine, by using a minimized objective function, a relevant parameter of the linear relationship in each window through a linear regression algorithm;
the kernel function calculation module 304 is configured to calculate a kernel function based on image-guided three-dimensional filtering according to the relevant parameters and the filtering kernel function;
and a filtering module 305, configured to filter the original post-stack seismic data by using the kernel function based on image-guided three-dimensional filtering, so as to obtain filtered post-stack seismic data.
In an alternative embodiment, the establishing module 302 includes:
a filter definition sub-module for defining the linear weighted average filter using pilot data;
and the adjustment sub-module is used for adjusting the weight coefficient of the linear weighted average filter by combining the regularization factor under the framework of the linear weighted average filter, and introducing the filtering kernel function of the explicit bilateral filter after adjustment.
In an alternative embodiment, the method further comprises:
and the analysis module is used for analyzing the filtered post-stack seismic data, including observation of spectral characteristics, amplitude variation and spatial distribution, and carrying out evaluation and analysis according to analysis results to obtain corresponding underground structure and seismic event information.
In an alternative embodiment, the filter kernel function is:
the expression of the linear weighted average filter is as follows:
wherein W is i，j (G) For a filter kernel with respect to G, G is the pilot data,is window w k The number of data points in the image, k is the pixel position in the image, G i For a pixel value in the pilot data at the ith point, G j For a pixel value, m, in the pilot data at the ith point k And->Respectively window w k Mean and variance of the internal data G, E is regularization factor, < >>For the input data at the j-th point, < +.>Is the output data at the i-th point.
The embodiment of the invention also provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and the computer program when executed by the processor causes the processor to execute the steps of the post-stack seismic data processing method based on the image-guided three-dimensional filtering in any embodiment.
The embodiment of the invention also provides a computer storage medium, on which a computer program is stored, the computer program, when executed by the processor, implementing the steps of a post-stack seismic data processing method based on image-guided three-dimensional filtering according to any of the above embodiments.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments provided in the present application, it should be understood that the methods, apparatuses, electronic devices and storage media disclosed in the present application may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a readable storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A post-stack seismic data processing method based on image-guided three-dimensional filtering, comprising:
acquiring original post-stack seismic data, guide data and regularization factors;
based on the guide data and the regularization factor, establishing a linear weighted average filter and a filtering kernel function thereof;
determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm;
calculating to obtain a kernel function based on image-guided three-dimensional filtering through the related parameters and the filtering kernel function;
and filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the filtered post-stack seismic data.
2. The method of image guided three-dimensional filtering based post-stack seismic data processing of claim 1, wherein establishing a linear weighted average filter and its filter kernel function based on the guide data and the regularization factor comprises:
defining the linear weighted average filter using pilot data;
and under the framework of the linear weighted average filter, combining the regularization factors, adjusting the weight coefficient of the linear weighted average filter, and introducing the filtering kernel function of an explicit bilateral filter after adjustment.
3. The method for processing post-stack seismic data based on image-guided three-dimensional filtering according to claim 1, wherein filtering the original post-stack seismic data using the kernel based on image-guided three-dimensional filtering, after obtaining the filtered post-stack seismic data, further comprises:
and analyzing the filtered post-stack seismic data, including observing spectral characteristics, amplitude changes and spatial distribution, and carrying out evaluation and analysis according to analysis results to obtain corresponding underground structure and seismic event information.
4. The method for processing post-stack seismic data based on image guided three-dimensional filtering according to claim 2, wherein the filtering kernel function is:
the expression of the linear weighted average filter is as follows:
wherein W is i,j (G) For a filter kernel with respect to G, G is the pilot data,is window w k The number of data points in the image, k is the pixel position in the image, G i For a pixel value in the pilot data at the ith point, G j For a pixel value, m, in the pilot data at the ith point k And->Respectively window w k Mean and variance of the internal data G, E is regularization factor, < >>For the input data at the j-th point, < +.>Is the output data at the i-th point.
5. A post-stack seismic data processing device based on image-guided three-dimensional filtering, comprising:
the acquisition module is used for acquiring the original post-stack seismic data, the guide data and the regularization factors;
the establishing module is used for establishing a linear weighted average filter and a filtering kernel function thereof based on the guide data and the regularization factor;
the parameter determining module is used for determining relevant parameters of the linear relation in each window by using a minimized objective function through a linear regression algorithm;
the kernel function calculation module is used for calculating a kernel function based on image-guided three-dimensional filtering through the related parameters and the filtering kernel function;
and the filtering module is used for filtering the original post-stack seismic data by using the kernel function based on the image-guided three-dimensional filtering to obtain the post-stack seismic data after filtering.
6. The image guided three-dimensional filtering based post-stack seismic data processing device of claim 5, wherein the build module comprises:
a filter definition sub-module for defining the linear weighted average filter using pilot data;
and the adjustment sub-module is used for adjusting the weight coefficient of the linear weighted average filter by combining the regularization factor under the framework of the linear weighted average filter, and introducing the filtering kernel function of the explicit bilateral filter after adjustment.
7. The image-guided three-dimensional filtering-based post-stack seismic data processing module of claim 5, further comprising:
and the analysis module is used for analyzing the filtered post-stack seismic data, including observation of spectral characteristics, amplitude variation and spatial distribution, and carrying out evaluation and analysis according to analysis results to obtain corresponding underground structure and seismic event information.
8. The image guided three-dimensional filtering based post-stack seismic data processing device of claim 6, wherein the filtering kernel function is:
the expression of the linear weighted average filter is as follows:
wherein W is i,j (G) For a filter kernel with respect to G, G is the pilot data,is window w k The number of data points in the image, j is the pixel position in the image, G i For a pixel value in the pilot data at the ith point, G j For a pixel value, m, in the pilot data at the j-th point k And->Respectively window w k Mean and variance of the internal data G, E is regularization factor, < >>For the input data at the j-th point, < +.>Is the output data at the i-th point.
9. An electronic device comprising a processor and a memory storing computer readable instructions that, when executed by the processor, perform the method of any of claims 1-4.
10. A storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of claims 1-4.
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