Pre-stack seismic reflection feature extraction method and device based on two-dimensional Gabor transformation

Abstract

The invention discloses a pre-stack seismic reflection feature extraction method and device based on two-dimensional Gabor transformation. The method comprises the steps of obtaining a two-dimensional image of pre-stack seismic data; performing convolution processing on the two-dimensional image by using two-dimensional Gabor kernels of different directions and different scales to obtain a plurality of Gabor features corresponding to different directions and different scales, the Gabor features being complex number domain values; calculating the amplitude of each Gabor feature, wherein the amplitude of each Gabor feature is used for representing the reflection feature in the corresponding direction and scale; and combining the amplitudes of all the Gabor characteristics to form a Gabor characteristic matrix so as to represent the integral reflection characteristics of the pre-stack seismic data. According to the method, the stability of seismic wave reflection feature extraction and the reliability of results can be improved, and geological abnormal information identification is realized.

Description

Technical Field

[0001] This invention relates to the field of reservoir interpretation of seismic data, and more specifically, to a method and apparatus for extracting pre-stack seismic reflection features based on two-dimensional Gabor transform. Background Technology

[0002] Traditional seismic reflection model analysis, i.e., seismic facies classification, is typically performed manually by interpreters. This requires experienced interpreters to spend considerable time and effort identifying seismic facies from massive amounts of seismic data. Even so, manual interpretation still struggles to detect numerous subtle differences within the data. This has spurred the development of automated seismic reflection model analysis methods. However, limited by seismic data quality and processing techniques, these methods usually rely on post-stack seismic data. While stacking helps suppress random noise to some extent, it also results in the loss of spatial information about amplitude variations with offset and azimuth found in pre-stack data.

[0003] With the development of seismic exploration technology in recent years, seismic data is developing towards wide azimuth, wide bandwidth, high density, and full wavefield. High-density, wide azimuth pre-stack seismic data containing rich subsurface feature information has become an inevitable trend to replace conventional post-stack data for seismic facies analysis. Summary of the Invention

[0004] The purpose of this invention is to propose a method and apparatus for pre-stack seismic reflection feature extraction based on two-dimensional Gabor transform. Addressing the problem of pre-stack multidimensional signal feature extraction, this invention studies a multidimensional seismic signal reflection mode analysis method, aiming to fully utilize and mine pre-stack and stratigraphic neighborhood reflection information, suppress unnecessary noise interference, improve the stability and reliability of seismic wave reflection feature extraction results, and achieve a more detailed reservoir feature description.

[0005] To achieve the above objectives, in a first aspect, this invention proposes a method for extracting pre-stack seismic reflection features based on two-dimensional Gabor transform, comprising:

[0006] Acquire two-dimensional images of pre-stack seismic data;

[0007] The two-dimensional image is convolved using two-dimensional Gabor kernels of different directions and scales to obtain multiple Gabor features corresponding to different directions and scales. The Gabor features are complex domain values.

[0008] Calculate the amplitude of each Gabor feature, which is used to characterize the reflection features at the corresponding direction and scale;

[0009] The amplitudes of all Gabor features are combined to form a Gabor feature matrix, which characterizes the overall reflection features of the pre-stack seismic data.

[0010] Optionally, acquiring two-dimensional images of pre-stack seismic data includes:

[0011] Based on the stratigraphic sequence of the pre-stack seismic data, the pre-stack seismic signal at the same reflection point is extracted, and the pre-stack seismic signal is used as a two-dimensional image.

[0012] Each column in the two-dimensional image represents a reflected waveform. The vertical changes in pixel values ​​in the two-dimensional image reflect the shape changes of the reflected waveform, and the horizontal changes in pixel values ​​reflect the changes in the waveform with the incident angle or measurement orientation.

[0013] Optionally, the functional expression of the two-dimensional Gabor kernel is:

[0014]

[0015] k v =k max / f v ,

[0016]

[0017] Where μ and v are the direction and scale of the Gabor kernel, respectively, δ is the radius of the Gaussian function, z = (x, y) are the two-dimensional data coordinates, ‖·‖ calculates the l2 norm, and k max The maximum frequency is f, where f is the frequency span factor of the core. v μ is the frequency span factor of the kernel in the v direction. max The number of directions of the Gabor core is defined. This refers to the oscillating component of the Gabor core. Let be the DC component of the Gabor kernel, such that the mean of the Gabor kernel is zero.

[0018] Optionally, the two-dimensional image can be convolved using the following formula:

[0019] G μ,v (z)=I(z)*Φ μ,v (z)

[0020] Where * represents convolution operation, G μ,v (z) is a complex domain value representing the Gabor features corresponding to direction μ and scale v, and I(z) represents the two-dimensional image corresponding to the pre-stack seismic data.

[0021] Optionally, the magnitude of each Gabor feature can be calculated using the following formula:

[0022]

[0023] Among them, M μ,v (z) represents the Gabor characteristic G μ,v The magnitude of (z), where Im{·} and Re{·} are complex numbers G μ,v The real and imaginary parts of (z).

[0024] Optionally, the Gabor feature matrix is:

[0025]

[0026] Where O is the Gabor characteristic matrix, μ max and v max This represents the maximum value of the Gabor core orientation and scale.

[0027] Optionally, the reflection features include: features of pre-stack seismic data reflection wave variation with offset and seismic reflection wave variation with azimuth.

[0028] Secondly, the present invention provides an electronic device, the electronic device comprising:

[0029] At least one processor; and,

[0030] A memory communicatively connected to the at least one processor; wherein,

[0031] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transform as described in any one of claims 1-5.

[0032] Thirdly, the present invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transform described in the first aspect.

[0033] Fourthly, this invention proposes a pre-stack seismic reflection feature extraction device based on two-dimensional Gabor transform, comprising:

[0034] The data acquisition module is used to acquire two-dimensional images of pre-stack seismic data;

[0035] The feature extraction module is used to perform convolution processing on the two-dimensional image using two-dimensional Gabor kernels of different directions and scales to obtain multiple Gabor features corresponding to different directions and scales, wherein the Gabor features are complex domain values.

[0036] The amplitude calculation module is used to calculate the amplitude of each Gabor feature. The amplitude of each Gabor feature is used to characterize the reflection characteristics in the corresponding direction and scale.

[0037] The feature unification module is used to combine the amplitudes of all Gabor features to form a Gabor feature matrix, which characterizes the overall reflection features of the pre-stack seismic data.

[0038] The beneficial effects of this invention are as follows:

[0039] This invention draws on image feature extraction methods and considers the characteristics of seismic signals. It uses a two-dimensional Gabor kernel to extract pre-stack seismic reflection features to identify geological anomalies. This method makes full use of the changes in pre-stack data with offset and azimuth to extract features that can characterize the changes in pre-stack seismic reflection waves with offset and azimuth. It also minimizes the impact of uncertainties (such as noise) in pre-stack data on feature extraction, making the features more robust and significantly improving the extraction effect on underground geological anomalies.

[0040] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0041] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0042] Figure 1 The diagram illustrates the steps of a pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transform according to the present invention.

[0043] Figure 2a The image shows a stacked set containing both non-cavities and cavities.

[0044] Figure 2b It shows the Figure 2a The result of Gabor feature extraction.

[0045] Figure 3a The image shows a pre-stack set containing both non-caves and cavities.

[0046] Figure 3b It shows the Figure 3a The result of Gabor feature extraction. Detailed Implementation

[0047] The improved quality of pre-stack seismic data enables more accurate subsurface reservoir information to be obtained through seismic reflection pattern analysis and feature extraction using pre-stack data. Therefore, this invention relies on wide-azimuth pre-stack 3D seismic data and studies robust feature extraction algorithms for pre-stack data to identify and extract pre-stack reflection patterns, providing a basis for seismic facies classification and guiding geological interpretation.

[0048] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0049] Example 1

[0050] like Figure 1 As shown, this embodiment provides a method for pre-stack seismic reflection feature extraction based on two-dimensional Gabor transform, including the following steps:

[0051] S1: Acquire a two-dimensional image of the pre-stack seismic data;

[0052] This step extracts the pre-stack seismic signal from the same reflection point based on the stratigraphic position of the pre-stack seismic data, and treats the pre-stack seismic signal as a two-dimensional image. Each column in the two-dimensional image is a reflection waveform. The vertical changes in pixel values ​​in the two-dimensional image reflect the shape changes of the reflection waveform, and the horizontal changes in pixel values ​​reflect the changes in the waveform with the incident angle or measurement azimuth.

[0053] Specifically, this method draws on image feature extraction methods and takes into account the characteristics of seismic signals. The subsequent steps use two-dimensional Gabor transform to extract pre-stack reflection features.

[0054] Two-dimensional Gabor wavelets closely resemble the visual stimulus responses of simple cells in the human visual system, exhibiting advantages in extracting local spatial and frequency domain information of targets. Two-dimensional Gabor wavelets offer excellent direction and scale selectivity, demonstrating strong expressive power for image features. In seismic exploration signal interpretation, by convolving seismic profile images with two-dimensional Gabor kernels of different directions and frequencies, a novel attribute can be obtained, which can be used to describe the boundary morphology of anomalous geological bodies. This invention selects multi-scale and multi-directional two-dimensional Gabor kernels to extract seismic signal features, forming a powerful pre-stack reflection feature representation.

[0055] In the pre-stack reflection mode analysis method based on two-dimensional Gabor features, pre-stack seismic data is treated as a two-dimensional image. This makes the method applicable to any form of pre-stack gather, such as CRP gathers, angle gathers, and wide azimuth gathers. These gathers not only contain information on amplitude variations over time but also information on amplitude variations with azimuth or incident angle. This feature information is encompassed in multi-directional, multi-scale Gabor features, and classifying Gabor features can better describe the characteristic details of the reservoir.

[0056] S2: Convolve the two-dimensional image using two-dimensional Gabor kernels of different directions and scales to obtain multiple Gabor features corresponding to different directions and scales, wherein the Gabor features are complex domain values;

[0057] The functional expression of the two-dimensional Gabor kernel is as follows:

[0058]

[0059] k v =k max / f v ,

[0060]

[0061] Where, μ and υ are the direction and scale of the Gabor kernel, respectively, δ is the radius of the Gaussian function, z = (x, y) are the two-dimensional data coordinates, ‖·‖ calculates the l2 norm, and k max The maximum frequency is f, where f is the frequency span factor of the core. v μ is the frequency span factor of the kernel in the v direction. max The number of directions of the Gabor core is defined. This refers to the oscillating component of the Gabor core. Let be the DC component of the Gabor kernel, such that the mean of the Gabor kernel is zero.

[0062] As can be seen from the above equation, the two-dimensional Gabor kernel function is the product of a Gaussian function and an exponential function. The Gaussian function ensures the locality of the Gabor kernel, while the exponential function is a frequency transform, guaranteeing the frequency selectivity of the Gabor kernel. The Gabor kernel uses a Gaussian function as a local window. Since the Fourier transform of the Gaussian function is still a Gaussian function, the Gabor transform can achieve the purpose of time-frequency localization.

[0063] This step performs convolution processing on the two-dimensional image using the following formula, utilizing the Gabor kernel Φ. μ,v (z) Convolution of the two-dimensional pre-stack seismic data I(z) yields the Gabor feature G. μ,v ,Right now:

[0064] G μ,v (z)=I(z)*Φ μ,v (z)

[0065] Where * represents convolution operation, G μ,v (z) is a complex domain value representing the Gabor features corresponding to direction μ and scale υ, and I(z) represents the two-dimensional image corresponding to the pre-stack seismic data.

[0066] S3: Calculate the amplitude of each Gabor feature. The amplitude of each Gabor feature is used to characterize the reflection characteristics at the corresponding direction and scale.

[0067] Specifically, typically only the amplitude of the Gabor feature is used as the feature representation, while the phase feature is discarded. In this step, the Gabor feature amplitude M... μ,v (z) is calculated as follows:

[0068]

[0069] Among them, M μ,v (z) represents the Gabor characteristic G μ,v The magnitude of (z), where Im{·} and Re{·} are complex numbers G μ,v The real and imaginary parts of (z).

[0070] S4: Combine the amplitudes of all Gabor features to form a Gabor feature matrix to characterize the overall reflection characteristics of the pre-stack seismic data.

[0071] The reflection features include: features of pre-stack seismic data reflection wave variation with offset and seismic reflection wave variation with azimuth.

[0072] Specifically, Gabor feature amplitudes are calculated using multi-directional, multi-scale Gabor kernels, and these feature amplitudes are combined to form the final Gabor feature representation O:

[0073]

[0074] Where O is the Gabor characteristic matrix, μ max and v max This represents the maximum value of the Gabor core orientation and scale.

[0075] This method fully utilizes the variation information of pre-stack data with offset and azimuth to extract features that can characterize the variation information of seismic reflection waves with offset and azimuth of pre-stack seismic data. It also minimizes the impact of uncertainties (such as noise) in pre-stack data on feature extraction, thereby making the features more robust and significantly improving the extraction effect of underground geological anomaly information.

[0076] Example 2

[0077] To illustrate the practical application of the method of this invention, this embodiment tests a geological model with and without karst caves, and analyzes its post-stack seismic data. Figure 2a Perform Gabor transform, and extract features based on the Gabor transform results. Figure 2b It can be seen that the characteristic differences between caves and non-caves are very small. However, using the pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transform of this invention, for pre-stack seismic azimuth gathers ( Figure 3a The results of Gabor transform feature extraction ( Figure 3b This indicates that the differences in characteristics between karst caves and non-karst caves become very obvious, demonstrating that the method of this invention can be used to identify geological anomalies in karst caves.

[0078] Example 3

[0079] This embodiment provides a pre-stack seismic reflection feature extraction device based on two-dimensional Gabor transform, including:

[0080] The data acquisition module is used to acquire two-dimensional images of pre-stack seismic data;

[0081] The feature extraction module is used to perform convolution processing on the two-dimensional image using two-dimensional Gabor kernels of different directions and scales to obtain multiple Gabor features corresponding to different directions and scales, wherein the Gabor features are complex domain values.

[0082] The amplitude calculation module is used to calculate the amplitude of each Gabor feature. The amplitude of each Gabor feature is used to characterize the reflection characteristics in the corresponding direction and scale.

[0083] The feature unification module is used to combine the amplitudes of all Gabor features to form a Gabor feature matrix, which characterizes the overall reflection features of the pre-stack seismic data.

[0084] For the specific functions of each module in this embodiment, please refer to Embodiment 1, which will not be repeated here.

[0085] Example 4

[0086] This embodiment provides an electronic device, the electronic device comprising:

[0087] At least one processor; and,

[0088] A memory communicatively connected to the at least one processor; wherein,

[0089] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transform as described in the above embodiments.

[0090] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0091] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0092] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0093] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0094] Example 5

[0095] This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute the pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transform described in the above embodiment.

[0096] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0097] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0098] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transform, characterized in that, The method comprises the following steps: obtaining a two-dimensional image of pre-stack seismic data; performing convolution processing on the two-dimensional image by using two-dimensional Gabor kernels of different directions and different scales respectively to obtain a plurality of Gabor features corresponding to different directions and different scales, the Gabor features being complex domain values; calculating the amplitude of each Gabor feature, the amplitude of each Gabor feature being used to represent the reflection feature under the corresponding direction and scale; combining the amplitudes of all Gabor features to form a Gabor feature matrix to represent the reflection feature of the whole pre-stack seismic data.

2. The method of claim 1, wherein, The step of obtaining the two-dimensional image of pre-stack seismic data comprises the following steps: extracting the pre-stack seismic signal of the same reflection point according to the horizon of the pre-stack seismic data, and taking the pre-stack seismic signal as a two-dimensional image; each column in the two-dimensional image is a reflection waveform, the longitudinal change of the pixel value in the two-dimensional image embodies the shape change of the reflection waveform, and the transverse change of the pixel value embodies the change of the waveform with the incident angle or the measurement direction.

3. The method of claim 1, wherein, The function expression of the two-dimensional Gabor kernel is: k v = k max / f v , where μ and υ are the orientation and scale of the Gabor kernel, respectively, δ is the radius of the Gaussian function, z = (x, y) is the two-dimensional data coordinate, || · || computes the l2-norm, k max is the maximum frequency, f is the frequency span factor of the kernel, f v is the frequency span factor of the kernel in the v direction, μ max defines the number of orientations of the Gabor kernel, is the oscillatory part of the Gabor kernel, is the DC component of the Gabor kernel, such that the mean of the Gabor kernel is zero.

4. The method of claim 3, wherein, the two-dimensional image is processed by the following formula: G μ,v (z) = I(z) * Φ μ,v (z) where * denotes convolution operation, G μ,v (z) is a complex-valued number, representing the Gabor feature corresponding to the direction μ and the scale υ, and I(z) represents the two-dimensional image corresponding to the pre-stack seismic data.

5. The method of claim 4, wherein, the amplitude of each Gabor feature is calculated by the following formula: where M μ,v (z) denotes the magnitude of the Gabor feature G μ,v (z), and Im{•} and Re{•} are the real and imaginary parts of the complex G μ,v (z).

6. The method of claim 5, wherein, the Gabor feature matrix is: where O is the Gabor feature matrix, μ max and v max are the maximum values of Gabor kernel orientation and scale.

7. The method of claim 1, wherein, The reflection feature includes the features of the reflection wave of the pre-stack seismic data changing with the offset distance and the seismic reflection wave changing with the azimuth angle.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transformation according to any one of claims 1-5.

9. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions for causing a computer to perform the pre-stack seismic reflection feature extraction method based on two-dimensional Gabor transformation according to any one of claims 1-5.

10. A device for extracting pre-stack seismic reflection features based on two-dimensional Gabor transform, characterized in that, The method comprises the following steps: a data acquisition module for obtaining a two-dimensional image of pre-stack seismic data; a feature extraction module for performing convolution processing on the two-dimensional image by using two-dimensional Gabor kernels of different directions and different scales respectively to obtain a plurality of Gabor features corresponding to different directions and different scales, the Gabor features being complex domain values; an amplitude calculation module for calculating the amplitude of each Gabor feature, the amplitude of each Gabor feature being used to represent the reflection feature under the corresponding direction and scale; a feature combination module for combining the amplitudes of all Gabor features to form a Gabor feature matrix to represent the reflection feature of the whole pre-stack seismic data.

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