A multi-seismic attribute fusion method and device, electronic equipment and storage medium

Abstract

This invention relates to the field of seismic exploration and discloses a method, apparatus, electronic device, and storage medium for multi-seismic attribute fusion. The multi-seismic attribute fusion method includes: acquiring multiple seismic attribute images of a target stratigraphic level in a target work area; constructing a Gaussian pyramid based on each seismic attribute image; constructing a Laplace pyramid for each seismic attribute image based on the Gaussian pyramid; fusing the Laplace pyramids of multiple seismic attribute images to obtain a fused Laplace pyramid; and reconstructing the fused Laplace pyramid to obtain the fusion result of the multiple seismic attribute images. This solves the problem of improving the prediction accuracy of reservoirs.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of seismic exploration, and particularly to a method, apparatus, electronic device and storage medium for multi-seismic attribute fusion. Background Technology

[0002] Seismic attribute fusion technology is an attribute analysis method that comprehensively considers the physical meaning of different attributes, selects seismic attributes with different characteristics that are sensitive to the target layer, and fuses them through certain mathematical operations so that the result simultaneously reflects the influence of each attribute on the reservoir.

[0003] One of the core aspects of seismic attribute fusion is the fusion algorithm. Traditional seismic attribute fusion algorithms are basically for point-to-point fusion, such as multiple linear regression. This is because linear regression of known well data and well-side attributes has clear mathematical meaning, but for thin layers with rapid lateral changes, it is far from meeting the requirements of theoretical research and actual production. Summary of the Invention

[0004] The purpose of this invention is to provide at least one method, apparatus, electronic device and storage medium for multi-seismic attribute fusion, which can at least solve the problem of improving the prediction accuracy of reservoirs.

[0005] To address the aforementioned technical problems, at least one embodiment of the present invention provides a multi-seismic attribute fusion method, comprising:

[0006] Obtain multiple seismic attribute images of the target layer in the target work area;

[0007] Construct a Gaussian pyramid based on images of each seismic attribute;

[0008] A Laplace pyramid is constructed based on the Gaussian pyramid for each seismic attribute image;

[0009] The Laplace pyramids from multiple seismic attribute images are fused to obtain the fused Laplace pyramid;

[0010] The fused Laplace pyramid is reconstructed to obtain a fusion result of multiple seismic attribute images.

[0011] In some optional embodiments, constructing the Gaussian pyramid based on each seismic attribute image includes:

[0012] For each type of seismic attribute image, the seismic attribute image of each layer is obtained through Gaussian smoothing and downsampling;

[0013] A Gaussian pyramid is constructed based on the seismic attribute images of each layer to represent each type of seismic attribute image.

[0014] In some alternative embodiments, constructing a Laplace pyramid for each seismic attribute data based on the Gaussian pyramid includes:

[0015] Upsample the first seismic attribute image of the uppermost layer in two adjacent layers of the Gaussian pyramid;

[0016] The difference between the first seismic attribute images before and after upsampling is used to obtain the Laplacian image of each layer;

[0017] A Laplacian pyramid is constructed based on the Laplacian image of each layer to represent the current seismic attributes.

[0018] In some optional embodiments, upsampling the first seismic attribute image of the upper layer in two adjacent layers of the Gaussian pyramid includes:

[0019] By interpolation, the resolution of the first seismic attribute image in the upper layer and the second seismic attribute image in the lower layer of the Gaussian pyramid are made consistent.

[0020] The interpolated first seismic attribute image is processed with a Gaussian convolution kernel to obtain the upsampled first seismic attribute image.

[0021] In some alternative embodiments, the Gaussian convolution kernel includes a Gaussian low-pass filter convolution kernel.

[0022] In some optional embodiments, the Laplacian pyramids of multiple seismic attribute images are fused to obtain a fused Laplacian pyramid, including:

[0023] The gradient fusion algorithm is used to fuse the topmost image of the Laplacian pyramid of multiple seismic attribute images to obtain the first fusion result;

[0024] The region energy algorithm is used to fuse images of each layer of the Laplacian pyramid with multiple seismic attributes, except for the top layer, to obtain a second fusion result;

[0025] The Laplace pyramid with multiple seismic attributes is obtained based on the first fusion result and the second fusion result.

[0026] At least one embodiment of the present invention also provides a multi-seismic attribute fusion device, comprising:

[0027] The acquisition module is used to acquire multiple seismic attribute images of the target layer in the target work area;

[0028] The first building module is used to construct a Gaussian pyramid based on images of each seismic attribute.

[0029] The second construction module is used to construct a Laplace pyramid for each seismic attribute image based on the Gaussian pyramid;

[0030] The fusion module is used to fuse Laplacian pyramids from multiple seismic attribute images to obtain a fused Laplacian pyramid.

[0031] The reconstruction module is used to reconstruct the fused Laplace pyramid to obtain a fusion result of multiple seismic attribute images.

[0032] At least one embodiment of the present invention also provides an electronic device, comprising:

[0033] At least one processor; and,

[0034] A memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the multi-seismic attribute fusion method described above.

[0036] At least one embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-seismic attribute fusion method.

[0037] At least one embodiment of the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described multi-seismic attribute fusion method.

[0038] This invention provides a multi-seismic attribute fusion method. The core of multi-seismic attribute fusion is to effectively identify target reservoirs. Traditional seismic attribute fusion algorithms are basically point-to-point fusion and cannot analyze planar attributes as a whole. However, this invention performs analysis and calculation from the global scope of planar attributes, which can improve the prediction accuracy of thin reservoirs and the results are more consistent with geological conditions. This invention has important practical application value in the multi-seismic attribute fusion of simulated data and actual data. Attached Figure Description

[0039] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0040] Figure 1 This is a flowchart of a multi-seismic attribute fusion method provided in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of a multi-seismic attribute fusion device provided in another embodiment of the present invention;

[0042] Figure 3 This is another embodiment of the present invention that provides the target horizon-sensitive seismic attribute RMS;

[0043] Figure 4 This is another embodiment of the present invention providing the target horizon-sensitive seismic attribute AVO;

[0044] Figure 5 This is another embodiment of the present invention providing the inversion of seismic attributes sensitive to the target horizon;

[0045] Figure 6 This is another embodiment of the present invention that provides gas-bearing properties of seismically sensitive stratigraphic layers;

[0046] Figure 7 This is another embodiment of the present invention, providing a five-layer Laplace's pyramid with four seismically sensitive properties;

[0047] Figure 8 This is the fusion result of a two-layer Laplace's Gaussian pyramid provided in another embodiment of the present invention;

[0048] Figure 9 This is the fusion result of a three-layer Laplace's Gaussian pyramid provided in another embodiment of the present invention;

[0049] Figure 10 This is the fusion result of a 4-layer Laplace's Gaussian pyramid provided in another embodiment of the present invention;

[0050] Figure 11 This is the fusion result of a 5-layer Laplace's pyramid provided in another embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0052] Seismic attribute fusion technology is an attribute analysis method that comprehensively considers the physical meaning of different attributes, selects seismic attributes with different characteristics that are sensitive to the target layer, and fuses them through certain mathematical operations so that the result simultaneously reflects the influence of each attribute on the reservoir.

[0053] One of the core aspects of seismic attribute fusion is the fusion algorithm. Traditional seismic attribute fusion algorithms are basically for point-to-point fusion, such as multiple linear regression. This is because linear regression of known well data and well-side attributes has clear mathematical meaning, but for thin layers with rapid lateral changes, it is far from meeting the requirements of theoretical research and actual production.

[0054] The prediction of reservoir regional characteristics and trends by seismic attributes cannot be represented by a single point in the underground space; it is jointly characterized and reflected by the interrelationships of multiple points within a certain area. The applicant discovered that seismic attributes can be characterized at multiple scales through Gaussian-Laplace pyramid decomposition. By constructing a pyramid of seismic attribute images and fusing the pyramids at each level using a specific mathematical algorithm, the fused results at each level are finally obtained through image reconstruction, resulting in a final fused result that better reflects actual geological conditions across the global scope of seismic attributes.

[0055] To address the aforementioned technical problems, this invention proposes a multi-seismic attribute fusion method. This method decomposes each attribute into multi-scale spatial frequency bands while preserving high-frequency detail information, and then fuses the various decomposed scales of the Laplace pyramid. The implementation details of the multi-seismic attribute fusion method in this embodiment are described below. These details are provided for ease of understanding and are not essential for implementing this solution. This invention...

[0056] Example 1

[0057] The multi-seismic attribute fusion method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 1 As shown, it includes:

[0058] Step 101: Obtain multiple seismic attribute images of the target layer in the target work area.

[0059] In practice, the acquired seismic attributes refer to seismic attributes that are sensitive to the target horizon. The original format of these seismic data is a two-dimensional matrix, which is then loaded as an image.

[0060] Step 102: Construct a Gaussian pyramid based on the image of each seismic attribute.

[0061] Gaussian pyramids are an efficient method for multi-scale, multi-resolution image analysis, consisting of a series of images arranged in a pyramid shape with progressively decreasing resolution.

[0062] In some specific implementations, a Gaussian pyramid is constructed based on the seismic attribute image for each type, including:

[0063] Step 102a: For each type of seismic attribute image, obtain the seismic attribute image of each layer through Gaussian smoothing and downsampling.

[0064] Step 102b: Construct a Gaussian pyramid corresponding to each seismic attribute image based on the seismic attribute image of each layer.

[0065] In practice, the Gaussian convolution kernel includes the Gaussian low-pass filter convolution kernel.

[0066] In one example, a certain sensitive seismic attribute of the target layer is defined as G0, G k-1 This represents the seismic sensitivity attribute of the (k-1)th layer, for G k-1 The seismic attributes of the k-th layer are obtained by Gaussian smoothing the seismic attributes of the k-th layer. k G k It is G k-1 1 / 4 of it has the following expression:

[0067]

[0068] In the formula, G k (i,j) represents the pixel value of pixel (i,j) in the seismic attribute image (target image) of layer k, where m and n are the parameters of the Gaussian convolution kernel, G k-1 (2i+m,2j+n) represents the pixel value of the pixel (2i+m,2j+n) in the seismic attribute image of the (k-1)th layer, G σ (x,y) is a Gaussian convolution kernel with low-pass filtering effect.

[0069] By continuously downsampling the smoothed seismic attribute images of each layer, the resulting seismic attribute images of each layer form a Gaussian pyramid.

[0070] In image processing, the construction of a Gaussian pyramid involves Gaussian blurring and continuous downsampling. These operations inevitably lead to the loss of high-frequency detail information in the image, negatively impacting the quality of subsequent image reconstruction. To accurately capture and describe this lost high-frequency information during pyramid construction, this embodiment introduces the concept of a Laplacian pyramid, aiming to losslessly restore the original properties of the image.

[0071] Step 103: Construct the Laplace pyramid for each seismic attribute image based on the Gaussian pyramid.

[0072] Each level of the Laplace pyramid corresponds to the residual between two adjacent levels in the Gaussian pyramid, and these residuals essentially reflect the high-frequency details lost during downsampling.

[0073] To accurately calculate these residuals, it is necessary to ensure that the resolution (i.e., the size of the two-dimensional matrix) of the sensitive attributes remains consistent between adjacent layers of the Gaussian pyramid before computation. Therefore, the upper layer image (between adjacent layers) in the Gaussian pyramid is upsampled to ensure that the upper and lower layers have the same spatial resolution when calculating the residuals.

[0074] In some specific implementations, a Laplace pyramid is constructed based on the Gaussian pyramid for each seismic attribute data, including:

[0075] Step 103a: Upsample the first seismic attribute image of the upper layer in two adjacent layers of the Gaussian pyramid.

[0076] In some specific implementations, the first seismic attribute image of the upper layer in two adjacent layers of the Gaussian pyramid is upsampled, including:

[0077] Through interpolation, the resolution of the first seismic attribute image in the upper layer and the second seismic attribute image in the lower layer of the Gaussian pyramid are made consistent; and

[0078] The interpolated first seismic attribute image is processed with a Gaussian convolution kernel to obtain the upsampled first seismic attribute image.

[0079] Step 103b: Subtract the first seismic attribute images before and after upsampling to obtain the Laplace image for each layer.

[0080] Step 103c: Construct a Laplacian pyramid of the current seismic attribute image based on the Laplacian image of each layer.

[0081] In a specific example, the seismic attribute image G of the k-th level of the Gaussian pyramid k The resolution is maintained with G through an interpolation function. k-1 The seismic attribute images of each layer have consistent resolution, and the interpolated seismic attribute values ​​are denoted as G. k-1 * The interpolated seismic attribute value G k-1 * The upsampled function G is obtained by performing a operation with a Gaussian convolution kernel. k * :

[0082]

[0083] For the upsampled attribute values, G k -G k *The Laplacian image of each layer is obtained, and several Laplacian images of the same resolution are fused to construct a Laplacian pyramid corresponding to each seismic attribute. This is also known as a Gaussian-Laplacian pyramid. Gaussian-Laplacian pyramid decomposition is a process of multi-scale representation of seismic attributes. By constructing a pyramid of seismic attribute images and fusing each level of the pyramid using a specific mathematical algorithm, the fused results at each level are finally obtained through image reconstruction, making the fused result more consistent with actual geological conditions in the global scope of seismic attributes.

[0084] Step 104: Fuse the Laplace pyramids from multiple seismic attribute images to obtain the fused Laplace pyramid.

[0085] In the specific implementation, the Laplacian pyramids of multiple seismic attribute images are fused to obtain a fused Laplacian pyramid, including:

[0086] Step 104a: Use the gradient fusion algorithm to fuse the topmost image of the Laplacian pyramid of multiple seismic attribute images to obtain the first fusion result.

[0087] Step 104b: Use the regional energy algorithm to fuse the images of each layer of the Laplacian pyramid with multiple seismic attributes, except for the top layer, to obtain the second fusion result.

[0088] Step 104c: Obtain the fused Laplace pyramid with multiple seismic attributes based on the first fusion result and the second fusion result.

[0089] In one example, the Laplacian pyramid contains n layers (n being the top layer). For the images below layer n (a total of n-1 layers), a region energy algorithm is applied. The specific steps are as follows: For a Gaussian Laplacian pyramid with a single seismic attribute, calculate the region energy E for each attribute point (pixel) in each layer of the pyramid. (i,j) This yields the region energy (E(i,j)) of several attribute points with the same attribute value. 1 , E(i,j) 2 …,E(i,j) n-1 This involves comparing the regional energy levels to determine the attribute value for a given point, and then filling it with the corresponding attribute value for a sensitive seismic attribute. For regional energy algorithms, there are...

[0090]

[0091] In the formula, p and q define the size of the region; w represents the weight; LA N Let (i,j) represent the nth layer of the Laplace pyramid; (i,j) represents the center point of the neighborhood, which is also the (i,j)th point of the energy matrix.

[0092] Furthermore, the fusion results of the Gaussian Laplace pyramid with several sensitive attributes below the nth level were obtained.

[0093] For the fusion of the nth layer of the Laplacian pyramid of Gauss, a gradient fusion algorithm, derived from Poisson image editing, is employed. This algorithm can naturally overlap the target region of the source image with the target image, effectively eliminating the boundary abruptness problem of the target region in the source image. The main idea is to reconstruct the image pixels within the synthesized region using interpolation methods based on the gradient information of the image and the boundary information of the target image. The specific steps are as follows: For the Laplacian pyramid of Gauss, the gradient of each attribute point in each layer of the pyramid is calculated, obtaining several attribute points with the same attribute value. The gradient magnitude is compared to determine the attribute value of that attribute point, which is then filled with the attribute value corresponding to the sensitive seismic attribute. For the gradient fusion algorithm, there are...

[0094]

[0095] In the formula, y represents the fusion result, v represents the reconstructed image, s represents the source image, t represents the objective function, i represents the seismic attribute value, and j represents the four neighbors of i. Ni represents the set of V(x, y), and S represents the set of boundaries surrounding v. For a reconstructed pixel v(x, y), if its corresponding neighborhood point s(x, y) is located in the source image, then the neighborhood gradient of s(x, y) is approximately equal to the neighborhood gradient of v(x, y); if the corresponding neighborhood point of s(x, y) is located outside the source image, i.e., a boundary point, then the neighborhood gradient of s(x, y) is approximately equal to the gradient between v(x, y) and the neighborhood points of the target image.

[0096] Thus, a fusion of several multi-layered Laplace pyramids of Gauss can be obtained.

[0097] Based on the fusion results obtained from the two algorithms, a fused Laplace pyramid is obtained.

[0098] Step 105: Reconstruct the fused Laplace pyramid to obtain the fusion result of multiple seismic attribute images.

[0099] In this embodiment, the attribute values ​​are calculated using the Laplace pyramid within the global scope of the seismic attribute image, which better reflects the actual geological conditions, making the fusion result more consistent with the actual geological conditions globally. The regional energy algorithm is applied to the attribute fusion of each layer, resulting in fast computation speed. The top layer of the Laplace pyramid is fused using a gradient fusion algorithm, which improves accuracy.

[0100] Example 2

[0101] Another embodiment of the present invention relates to a multi-seismic attribute fusion device. The implementation details of this multi-seismic attribute fusion device are described below. The following details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of the multi-seismic attribute fusion device in this embodiment can be seen as follows: Figure 2 As shown, it includes an acquisition module 201, a first construction module 202, a second construction module 203, a fusion module 204, and a reconstruction module 205.

[0102] The acquisition module 201 is used to acquire multiple seismic attribute images of the target layer in the target work area;

[0103] The first building module 202 is used to construct a Gaussian pyramid based on each seismic attribute image;

[0104] The second building module 203 is used to construct a Laplacian pyramid for each seismic attribute image based on the Gaussian pyramid;

[0105] The fusion module 204 is used to fuse Laplacian pyramids from multiple seismic attribute images to obtain a fused Laplacian pyramid.

[0106] Reconstruction module 205 is used to reconstruct the fused Laplace pyramid to obtain the fusion result of multiple seismic attribute images.

[0107] In practice, the acquired seismic attributes refer to seismic attributes that are sensitive to the target horizon. The original format of these seismic data is a two-dimensional matrix, which is then loaded as an image.

[0108] In some specific implementations, a Gaussian pyramid is constructed based on each type of seismic attribute image, including: obtaining the seismic attribute image of each layer through Gaussian smoothing and downsampling for each type of seismic attribute image; and constructing a Gaussian pyramid corresponding to each type of seismic attribute image based on the seismic attribute image of each layer.

[0109] In practice, the Gaussian convolution kernel includes the Gaussian low-pass filter convolution kernel.

[0110] In one example, a certain sensitive seismic attribute of the target layer is defined as G0, G k-1 This represents the seismic sensitivity attribute of the (k-1)th layer, for G k-1 The seismic attributes of the k-th layer are obtained by Gaussian smoothing the seismic attributes of the k-th layer. k G k It is G k-1 1 / 4 of it has the following expression:

[0111]

[0112] In the formula, G k(i,j) represents the pixel value of pixel (i,j) in the seismic attribute image (target image) of layer k, where m and n are the parameters of the Gaussian convolution kernel, G k-1 (2i+m,2j+n) represents the pixel value of the pixel (2i+m,2j+n) in the seismic attribute image of the (k-1)th layer, G σ (x,y) is a Gaussian convolution kernel with low-pass filtering effect.

[0113] By continuously downsampling the smoothed seismic attribute images of each layer, the resulting seismic attribute images of each layer form a Gaussian pyramid.

[0114] In some specific implementations, the Laplace pyramid for each type of seismic attribute data is constructed based on the Gaussian pyramid, including: upsampling the first seismic attribute image of the upper layer in two adjacent layers of the Gaussian pyramid; subtracting the first seismic attribute images before and after upsampling to obtain the Laplace image of each layer; and constructing the Laplace pyramid of the current seismic attribute image based on the Laplace image of each layer.

[0115] In some specific implementations, upsampling is performed on the first seismic attribute image of the upper layer in two adjacent layers of the Gaussian pyramid. This includes: processing the resolution of the first seismic attribute image of the upper layer and the second seismic attribute image of the lower layer in two adjacent layers of the Gaussian pyramid to be consistent through interpolation; and performing a operation between the interpolated first seismic attribute image and a Gaussian convolution kernel to obtain the upsampled first seismic attribute image.

[0116] In a specific example, the seismic attribute image G of the k-th level of the Gaussian pyramid k The resolution is maintained with G through an interpolation function. k-1 The seismic attribute images of each layer have consistent resolution, and the interpolated seismic attribute values ​​are denoted as G. k-1 * The interpolated seismic attribute value G k-1 * The upsampled function G is obtained by performing a operation with a Gaussian convolution kernel. k * :

[0117]

[0118] For the upsampled attribute values, G k -G k * The Laplacian image of each layer is obtained, and several Laplacian images of the same resolution are fused together to construct the Laplacian pyramid corresponding to each seismic attribute.

[0119] In the specific implementation, the Laplacian pyramids of multiple seismic attribute images are fused to obtain a fused Laplacian pyramid, including: fusing the topmost image of the Laplacian pyramid of multiple seismic attribute images using a gradient fusion algorithm to obtain a first fusion result; fusing each layer of the Laplacian pyramid of multiple seismic attributes except the topmost layer using a region energy algorithm to obtain a second fusion result; and obtaining a fused multi-seismic attribute Laplacian pyramid based on the first fusion result and the second fusion result.

[0120] In this embodiment, the attribute values ​​are calculated using the Laplace pyramid within the global scope of the seismic attribute image, which better reflects the actual geological conditions, making the fusion result more consistent with the actual geological conditions globally. The regional energy algorithm is applied to the attribute fusion of each layer, resulting in fast computation speed. The top layer of the Laplace pyramid is fused using a gradient fusion algorithm, which improves accuracy.

[0121] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0122] Example 3

[0123] Another embodiment of the present invention relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the multi-seismic attribute fusion methods described in the above embodiments.

[0124] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0125] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0126] Example 4

[0127] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the above-described embodiment of the multi-seismic attribute fusion method.

[0128] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] At least one embodiment of the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described multi-seismic attribute fusion method.

[0130] Example 5

[0131] This embodiment provides an example to verify the feasibility of the method of the present invention.

[0132] The study area is sensitive to various seismic attributes of the target layer, such as... Figures 3 to 6 As shown, this includes RMS, AVO, inversion, and gas content. The constructed 5-layer Laplace pyramid is as follows: Figure 7 As shown. The final fusion result is as follows. Figures 8 to 11 As shown, because the Laplacian Gaussian pyramid of this invention uses a Gaussian low-pass filter convolution kernel, the frequency of the fusion result gradually decreases from bottom to top. In application, the appropriate kernel can be selected based on actual needs. Figures 8 to 11 The achievements in this regard should be understood. Figures 8 to 11 The fusion results shown refer to the fusion results from the bottom layer upwards, in order to... Figure 8 For example, it shows the fusion result of the bottom layer and the two layers above it in the Laplace pyramid. As the number of layers increases, low-frequency information becomes richer. For instance, if only the favorable boundary of the target layer needs to be understood, using... Figure 11That's sufficient. If further analysis of the fusion details is required, it can be done using... Figure 8 Perform the analysis.

[0133] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A multi-seismic attribute fusion method, characterized in that, include: Obtain multiple seismic attribute images of the target layer in the target work area; Construct a Gaussian pyramid based on images of each seismic attribute; A Laplace pyramid is constructed based on the Gaussian pyramid for each seismic attribute image; The Laplace pyramids from multiple seismic attribute images are fused to obtain the fused Laplace pyramid; The fused Laplace pyramid is reconstructed to obtain a fusion result of multiple seismic attribute images.

2. The multi-seismic attribute fusion method according to claim 1, characterized in that, The construction of the Gaussian pyramid based on each seismic attribute image includes: For each type of seismic attribute image, the seismic attribute image of each layer is obtained through Gaussian smoothing and downsampling; A Gaussian pyramid is constructed based on the seismic attribute images of each layer to represent each type of seismic attribute image.

3. The multi-seismic attribute fusion method according to claim 2, characterized in that, Based on the Gaussian pyramid, a Laplace pyramid is constructed for each seismic attribute data, including: Upsample the first seismic attribute image of the uppermost layer in two adjacent layers of the Gaussian pyramid; The difference between the first seismic attribute images before and after upsampling is used to obtain the Laplacian image of each layer; A Laplacian pyramid is constructed based on the Laplacian image of each layer to represent the current seismic attributes.

4. The multi-seismic attribute fusion method according to claim 3, characterized in that, The upsampling of the first seismic attribute image of the upper layer in two adjacent layers of the Gaussian pyramid includes: By interpolation, the resolution of the first seismic attribute image in the upper layer and the second seismic attribute image in the lower layer of the Gaussian pyramid are made consistent. The interpolated first seismic attribute image is processed with a Gaussian convolution kernel to obtain the upsampled first seismic attribute image.

5. The multi-seismic attribute fusion method according to claim 4, characterized in that, The Gaussian convolution kernel includes a Gaussian low-pass filter convolution kernel.

6. The multi-seismic attribute fusion method according to claim 1, characterized in that, By fusing Laplace pyramids from multiple seismic attribute images, a fused Laplace pyramid is obtained, including: The gradient fusion algorithm is used to fuse the topmost image of the Laplacian pyramid of multiple seismic attribute images to obtain the first fusion result; The region energy algorithm is used to fuse images of each layer of the Laplacian pyramid with multiple seismic attributes, except for the top layer, to obtain a second fusion result; Based on the first fusion result and the second fusion result, a Laplace pyramid with multiple seismic attributes is obtained.

7. A multi-seismic attribute fusion device, characterized in that, include: The acquisition module is used to acquire multiple seismic attribute images of the target layer in the target work area; The first building module is used to construct a Gaussian pyramid based on images of each seismic attribute. The second construction module is used to construct a Laplace pyramid for each seismic attribute image based on the Gaussian pyramid; The fusion module is used to fuse Laplacian pyramids from multiple seismic attribute images to obtain a fused Laplacian pyramid. The reconstruction module is used to reconstruct the fused Laplace pyramid to obtain a fusion result of multiple seismic attribute images.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the multi-seismic attribute fusion method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the multi-seismic attribute fusion method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the multi-seismic attribute fusion method as described in any one of claims 1 to 6.

Images