Orientation attribute dimension reduction method and system for ellipse analysis

Abstract

The invention relates to the field of seismic exploration, and provides an orientation attribute dimension reduction method and system based on ellipse analysis. The method comprises the following steps: acquiring a five-dimensional pre-stack gather according to seismic data; determining an orientation attribute body according to the five-dimensional pre-stack gather; determining the anisotropic contribution rate of the azimuth attribute body according to the five-dimensional pre-stack gather; and according to the anisotropic contribution rate of the azimuth body, determining a dimension reduction result of the azimuth attribute body. According to the method, the seismic data of two widths and one height can be efficiently utilized, azimuth angle information in the pre-stack seismic data is fully excavated, and then effective characterization of complex geological anomalous bodies such as fractures-cracks and river channels can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of seismic exploration, and in particular, to a method and system for dimension reduction of azimuth attributes based on ellipse analysis. Background Art

[0002] In order to efficiently utilize "two-wide and one-high" seismic data, dimensionality reduction research is carried out on multiple azimuth attribute volumes to fully exploit the azimuth angle information in pre-stack seismic data and achieve effective characterization of complex geological anomalies such as faults-fractures and channels. For the dimensionality reduction research of the azimuth body stack, according to related technologies, direct post-stack of direct attributes or RGB three-color fusion is mostly directly adopted. Traditional dimensionality reduction means have low accuracy, and there is mutual interference in the images of the mixed results. RGB fusion is only a visual fusion at the display level and is not a true attribute fusion dimensionality reduction technology. It is impossible to achieve dimensionality reduction and optimization fusion of multiple azimuth attribute volumes.

[0003] The information disclosed in the background art part of the present application is only intended to deepen the understanding of the general background art of the present application, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0004] The present invention provides a method and system for dimension reduction of azimuth attributes based on ellipse analysis, which can solve the technical problem that the dimensionality reduction and optimization fusion of multiple azimuth attribute volumes cannot be achieved.

[0005] The first aspect of the present invention provides a method for dimension reduction of azimuth attributes based on ellipse analysis, including:

[0006] Obtain a five-dimensional pre-stack trace gather according to seismic data;

[0007] Determine an azimuth attribute volume according to the five-dimensional pre-stack trace gather;

[0008] Determine the anisotropic contribution rate of the azimuth attribute volume according to the five-dimensional pre-stack trace gather;

[0009] Determine the dimensionality reduction result of the azimuth attribute volume according to the anisotropic contribution rate of the azimuth body. Determining an azimuth attribute volume according to the five-dimensional pre-stack trace gather includes:

[0010] According to a preferred embodiment of the present invention, obtain a pre-stack azimuth offset seismic trace gather after migration according to the five-dimensional pre-stack trace gather;

[0011] Determine a partial azimuth stack according to the pre-stack azimuth offset seismic trace gather after migration;

[0012] Determine the azimuth attribute volume according to the partial azimuth stack.

[0013] According to a preferred embodiment of the present invention, determining a partial azimuth stack body according to the offset pre-stack azimuth offset seismic gather includes:

[0014] Stacking the offset pre-stack azimuth offset seismic gather according to a preset partial azimuth angle to obtain the partial azimuth stack body.

[0015] According to a preferred embodiment of the present invention, determining the azimuth attribute body according to the partial azimuth stack body includes:

[0016] Obtaining the azimuth attribute body according to one or more of eigen-coherence attribute calculation, curvature attribute calculation, seismic tensor attribute calculation, edge detection attribute calculation, and ant attribute calculation performed on the partial azimuth stack body.

[0017] According to a preferred embodiment of the present invention, determining the anisotropy contribution rate of the azimuth attribute body according to the five-dimensional pre-stack gather includes:

[0018] Determining the anisotropy intensity and azimuth angle according to the five-dimensional pre-stack gather;

[0019] Determining the anisotropy contribution rate of the azimuth attribute body according to the anisotropy intensity and azimuth angle.

[0020] According to a preferred embodiment of the present invention, determining the anisotropy contribution rate of the azimuth attribute body according to the anisotropy intensity and azimuth angle includes:

[0021] Determining the anisotropy intensity as a value of non-ellipticity;

[0022] Determining the major axis value of the ellipse according to the azimuth angle and the value of non-ellipticity;

[0023] Determining the major axis value of the ellipse as the anisotropy value;

[0024] Determining the maximum value of the anisotropy values and the anisotropy values to determine the anisotropy contribution rate.

[0025] According to a preferred embodiment of the present invention, determining the dimensionality reduction result of the azimuth attribute body according to the anisotropy contribution rate of the azimuth body includes:

[0026] Obtaining the result of fusing the azimuth attributes of each azimuth according to the anisotropy contribution rate of each azimuth:

[0027] Obtaining the dimensionality reduction result of the azimuth attribute according to the result of fusing the azimuth attributes of each azimuth.

[0028] According to a preferred embodiment of the present invention, obtaining the result of fusing the azimuth attributes of each azimuth according to the anisotropy contribution rate of each azimuth includes:

[0029] According to the respective azimuth anisotropy contribution rates, linear superposition is performed on the azimuth attribute volumes to obtain the result after fusion of the azimuth attributes.

[0030] The second aspect of the present invention provides an azimuth attribute dimensionality reduction system based on elliptical analysis, including:

[0031] A seismic data processing module, which obtains a five-dimensional pre-stack gather according to seismic data;

[0032] An azimuth attribute volume determination module, which determines an azimuth attribute volume according to the five-dimensional pre-stack gather;

[0033] An anisotropy contribution rate determination module, which determines the anisotropy contribution rate of the azimuth attribute volume according to the five-dimensional pre-stack gather;

[0034] A dimensionality reduction result determination module, which determines the dimensionality reduction result of the azimuth attribute volume according to the anisotropy contribution rate of the azimuth body.

[0035] According to a preferred embodiment of the present invention, the azimuth attribute volume determination module is further configured to obtain a pre-stack azimuth offset seismic gather after migration according to the five-dimensional pre-stack gather;

[0036] According to the pre-stack azimuth offset seismic gather after migration, a partial azimuth stack body is determined;

[0037] According to the partial azimuth stack body, the azimuth attribute volume is determined.

[0038] Determining a partial azimuth stack body according to the pre-stack azimuth offset seismic gather after migration includes:

[0039] The pre-stack azimuth offset seismic gather after migration is stacked according to a preset partial azimuth angle to obtain the partial azimuth stack body.

[0040] Determining the azimuth attribute volume according to the partial azimuth stack body includes:

[0041] The azimuth attribute volume is obtained according to one or more of intrinsic coherence attribute calculation, curvature attribute calculation, seismic tensor attribute calculation, edge detection attribute calculation, and ant attribute calculation performed on the partial azimuth stack body.

[0042] According to a preferred embodiment of the present invention, the anisotropy contribution rate determination module is further configured to determine the anisotropy intensity and azimuth angle according to the five-dimensional pre-stack gather;

[0043] According to the anisotropy intensity and azimuth angle, the anisotropy contribution rate of the azimuth attribute volume is determined.

[0044] Determine the anisotropic contribution rate of the azimuth attribute body according to the anisotropic intensity and azimuth angle, including:

[0045] Determine the anisotropic intensity as a value of non-ellipticity;

[0046] Determine the major axis value of the ellipse according to the azimuth angle and the value of non-ellipticity;

[0047] Determine the major axis value of the ellipse as the anisotropic value;

[0048] Determine the maximum value of the anisotropic values and the anisotropic values to determine the anisotropic contribution rate.

[0049] According to a preferred embodiment of the present invention, the dimensionality reduction result determination module is further configured to determine the dimensionality reduction result of the azimuth attribute body according to the anisotropic contribution rate of the azimuth body, including:

[0050] Obtain the result after fusion of the azimuth attributes according to the anisotropic contribution rates of the respective azimuths:

[0051] Obtain the dimensionality reduction result of the azimuth attribute according to the result after fusion of the azimuth attributes.

[0052] Obtain the result after fusion of the azimuth attributes according to the anisotropic contribution rates of the respective azimuths, including:

[0053] Perform linear superposition on the azimuth attribute bodies according to the anisotropic contribution rates of the respective azimuths to obtain the result after fusion of the azimuth attributes.

[0054] A third aspect of the present invention provides an azimuth attribute dimensionality reduction device based on ellipse analysis, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to call the instructions stored in the memory to execute the azimuth attribute dimensionality reduction method based on ellipse analysis.

[0055] According to a preferred embodiment of the present invention, there is provided a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the azimuth attribute dimensionality reduction method based on ellipse analysis is implemented.

[0056] The purpose of the present invention is to achieve dimensionality reduction and optimized fusion of multiple azimuth attribute bodies, and is also for efficient utilization of "two-wide and one-high" seismic data, fully mining the azimuth angle information in pre-stack seismic data, and further enabling effective characterization of complex geological anomalies such as faults-fractures and channels.

[0057] The beneficial effects of the present invention at least include: It is possible to perform all-round processing on seismic data of a "two-wide and one-high" seismic acquisition system to obtain a five-dimensional seismic trace gather. Stack the pre-stack seismic trace gather according to N different finite azimuth angles to obtain an azimuth stack body, and then perform attribute calculation to obtain an azimuth attribute body. Perform AVAZ inversion on the five-dimensional seismic trace gather to obtain an anisotropic body and a fracture azimuth body. Based on the ellipticity concept of elliptic fitting, perform regression analysis on anisotropy and azimuth to obtain the anisotropic contribution rates of different azimuth attribute bodies and perform linear summation to obtain the dimensionality reduction result of the azimuth attribute. According to the present invention, it is possible to efficiently utilize "two-wide and one-high" seismic data, fully exploit the azimuth angle information in pre-stack seismic data, and thereby effectively characterize complex geological anomalies such as faults-fractures and channels. When obtaining a five-dimensional pre-stack trace gather according to seismic data, it is possible to comprehensively process the seismic data to obtain a five-dimensional seismic trace gather after all-round processing of the seismic data, providing a more comprehensive data basis for obtaining the dimensionality reduction result. When determining the azimuth attribute body according to the five-dimensional pre-stack trace gather, it is possible to carry out three-dimensional high-precision attribute calculation to obtain the azimuth attribute body, improving the accuracy of the azimuth attribute body. When determining the anisotropic strength and azimuth angle, it is possible to carry out fracture inversion through various methods to obtain the anisotropic strength and azimuth angle, more conveniently providing a data basis for subsequent dimensionality reduction and fusion. When determining the anisotropic contribution rate of the azimuth attribute body, it is possible to use the method of elliptic fitting, calculate the magnitudes of two sets of ratio values simultaneously, and then calculate the anisotropic contribution rates of two several azimuth bodies respectively. After cycling through all steps and repeating the entire three-dimensional body line direction, the azimuth anisotropic contribution rates of all CDP points of the entire three-dimensional body can be obtained. Improve the objectivity, accuracy, and comprehensiveness of the obtained anisotropic contribution rate. When determining the dimensionality reduction result, it is possible to obtain the result after fusion of the azimuth attribute by linear summation, and then obtain the correct result through comparative analysis, improving the accuracy and objectivity of the dimensionality reduction result.

[0058] The method and apparatus of the present invention have other characteristics and advantages, which will be apparent in the accompanying drawings and subsequent detailed embodiments incorporated herein, or will be described in detail in the accompanying drawings and subsequent detailed embodiments incorporated herein. These accompanying drawings and detailed embodiments are used together to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more apparent.

[0060] Figure 1 FIG. shows a schematic flow chart of an azimuth attribute dimensionality reduction method based on elliptic analysis according to an embodiment of the present invention;

[0061] Figures 2A - 2F A schematic diagram showing attribute slices at multiple azimuth angles according to an embodiment of the present invention is shown;

[0062] Figure 3 It shows the slice along the layer based on the coherent attribute of the post-stack data according to an embodiment of the present invention;

[0063] Figure 4 A schematic diagram of an ellipse according to an embodiment of the present invention is shown;

[0064] Figure 5 A system schematic diagram of an orientation attribute dimension reduction method based on ellipse analysis according to an embodiment of the present invention is exemplarily shown;

[0065] Figure 6 A block diagram of a device for reducing the dimension of orientation attributes based on ellipse analysis according to an embodiment of the present disclosure is shown;

[0066] Figure 7 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0067] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0068] Example 1

[0069] Figure 1 The flowchart of the method for reducing the dimension of orientation attributes based on ellipse analysis according to an embodiment of the present invention is shown.

[0070] Step S101, obtaining a five-dimensional pre-stack gather based on seismic data;

[0071] Step S102, determining an azimuth attribute body according to the five-dimensional pre-stack gathers;

[0072] Step S103, determining the anisotropic contribution rate of the azimuth attribute volume according to the five-dimensional pre-stack gathers;

[0073] Step S104: determining a dimensionality reduction result of the orientation attribute volume according to the anisotropic contribution rate of the orientation volume.

[0074] According to the azimuth attribute dimensionality reduction method based on ellipse analysis in the embodiments of the present invention, seismic data of a "two-wide and one-high" seismic acquisition system can be processed in all directions to obtain a five-dimensional seismic trace gather. The prestack seismic trace gather is stacked according to N different finite azimuth angles to obtain an azimuth stack body, and then attribute calculation is performed to obtain an azimuth attribute body. AVAZ inversion is performed on the five-dimensional seismic trace gather to obtain an anisotropic body and a fracture azimuth body. Based on the ellipticity concept of ellipse fitting, regression analysis is performed on anisotropy and azimuth, and the anisotropy contribution rates of different azimuth attribute bodies are linearly added to obtain the dimensionality reduction result of the azimuth attribute. According to the present invention, it is possible to efficiently utilize "two-wide and one-high" seismic data, fully exploit the azimuth angle information in prestack seismic data, and further effectively characterize complex geological anomalies such as faults-fractures and channels.

[0075] According to an embodiment of the present invention, in step S101, according to seismic data, a five-dimensional prestack trace gather is obtained. Processing the seismic data of "two-wide and one-high" usually includes, but is not limited to, trace editing, band-pass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, prestack deconvolution, and dynamic correction, etc., to obtain a five-dimensional prestack trace gather. The formation of the five-dimensional prestack trace gather requires performing full-direction migration imaging and retaining the azimuth angle and offset. Furthermore, a five-dimensional seismic trace gather (XYZ in three-dimensional space plus azimuth angle plus offset) after processing the seismic data in all directions can be obtained.

[0076] In this way, the seismic data can be comprehensively processed to obtain a five-dimensional seismic trace gather after processing the seismic data in all directions, providing a more comprehensive data basis for obtaining the dimensionality reduction result.

[0077] According to an embodiment of the present invention, in step S102, according to the five-dimensional prestack trace gather, determining the azimuth attribute body includes:

[0078] Obtaining a prestack azimuth-offset seismic trace gather after offset according to the five-dimensional prestack trace gather;

[0079] Determining a partial azimuth stack body according to the prestack azimuth-offset seismic trace gather after offset;

[0080] Determining the azimuth attribute body according to the partial azimuth stack body.

[0081] According to an embodiment of the present invention, determining a partial azimuth stack body according to the prestack azimuth-offset seismic trace gather after offset includes:

[0082] Stacking the prestack azimuth-offset seismic trace gather after offset according to a preset partial azimuth angle to obtain the partial azimuth stack body.

[0083] According to an embodiment of the present invention, a limited azimuth stack generates an azimuth stack body. By stacking the pre-stack seismic trace gathers according to N different limited azimuth angles (the number of azimuth angles ranges from 3 to 8, and the more azimuth angles, the greater the computational load), an azimuth stack body can be produced. (This patent only performs attribute dimensionality reduction for the azimuth stack body and does not perform dimensionality reduction operations on the offset stack body). For the pre-stack azimuth offset (azimuth incident angle) seismic trace gathers after migration, stack them according to partial azimuth angles to form a partial azimuth stack body.

[0084] Figures 2A - 2F The schematic diagram shows the attribute slices of multiple azimuth angles according to an embodiment of the present invention. Figure 3 The layer slice based on the post-stack data coherence attribute according to an embodiment of the present invention is shown.

[0085] For example, a total of 6 azimuth stack bodies can be formed, which are respectively

[0086] 1 to 30 degrees azimuth, and the azimuth angle is recorded as 15 degrees (such as Figure 2A );

[0087] 31 to 60 degrees stack, and the azimuth angle is 45 degrees (such as Figure 2B );

[0088] 61 to 90 degrees stack, and the azimuth angle is recorded as 75 degrees (such as Figure 2C );

[0089] 91 degrees to 120 degrees stack, and the azimuth angle is recorded as 115 degrees (such as Figure 2D );

[0090] 121 to 150 degrees stack, and the azimuth angle is 135 degrees (such as Figure 2E );

[0091] 151 to 180 degrees stack, and the azimuth angle is recorded as 165 degrees (such as Figure 2F );

[0092] According to an embodiment of the present invention, the azimuth attribute body is determined according to the partial azimuth stack body. It includes:

[0093] The azimuth attribute body obtained by performing one or more of eigen-coherence attribute calculation, curvature attribute calculation, seismic tensor attribute calculation, edge detection attribute calculation, and ant attribute calculation on the partial azimuth stack body.

[0094] According to an embodiment of the present invention, an azimuth attribute body is formed through attribute calculation. For the azimuth superposition body after finite azimuth superposition, its data itself is the same as the conventional post-stack three-dimensional data body. Therefore, three-dimensional high-precision attribute calculation can be carried out based on the azimuth superposition body, such as attributes like eigen coherence, curvature, seismic tensor, edge detection, and ants, and then the azimuth attribute body is obtained. For example, six azimuth attribute bodies are formed after carrying out three-dimensional high-precision attribute calculation based on the azimuth superposition body.

[0095] In this way, three-dimensional high-precision attribute calculation can be carried out to obtain the azimuth attribute body, improving the accuracy of the azimuth attribute body.

[0096] According to an embodiment of the present invention, in step S103, determining the anisotropy contribution rate of the azimuth attribute body according to the five-dimensional pre-stack gather includes:

[0097] Determining the anisotropy strength and azimuth angle according to the five-dimensional pre-stack gather;

[0098] Determining the anisotropy contribution rate of the azimuth attribute body according to the anisotropy strength and azimuth angle.

[0099] According to an embodiment of the present invention, for the five-dimensional pre-stack gather after omnidirectional migration imaging processing, fracture inversion can be carried out to obtain the anisotropy strength and azimuth angle. The fracture strength (i.e., anisotropy strength) and fracture azimuth can be obtained through a patent algorithm. For example, based on data-driven large-volume structure fracture depth inversion, the fracture strength (i.e., anisotropy strength) and fracture azimuth can also be obtained through conventional AVAZ inversion.

[0100] In this way, fracture inversion can be carried out through multiple methods to obtain the anisotropy strength and azimuth angle, providing a more convenient data basis for subsequent dimensionality reduction and fusion.

[0101] According to an embodiment of the present invention, determining the anisotropy contribution rate of the azimuth attribute body according to the anisotropy strength and azimuth angle includes:

[0102] Determining the anisotropy strength as the value of non-ellipticity;

[0103] Determining the major axis value of the ellipse according to the azimuth angle and the value of non-ellipticity;

[0104] Determining the major axis value of the ellipse as the anisotropy value;

[0105] Determining the maximum value of the anisotropy values and the anisotropy values to determine the anisotropy contribution rate.

[0106] Figure 4 A schematic diagram of an ellipse according to an embodiment of the present invention is shown.

[0107] According to an embodiment of the present invention, the early anisotropy-based fracture parameter inversion is based on ellipse fitting, where the fracture intensity (i.e., anisotropy intensity) is the non-ellipticity, which is defined as the major axis / minor axis, and the definition of the azimuth angle is the same as that in step S102. Drawing on the concept of non-ellipticity, the anisotropy intensity obtained based on step S102 is denoted as the value of non-ellipticity. For the sake of easy understanding, as Figure 3 shown in the ellipse, the length of the major axis is a, and the minor axis is b. Therefore, the ellipticity η of the ellipse = b / a, and the azimuth angle of this ellipse is For a point M with an axis length of ρ, its azimuth angle Azi can be denoted as Therefore, for point M, its included angle According to the standard equation of the ellipse, the coordinates x and y of point M at this time have the following relationship:

[0108]

[0109] Since the axis length of M is ρ, then x = ρcosθ, y = ρsinθ; given a and b, substituting them into the above standard equation, the value of ρ can be obtained. For example, when there are a total of six azimuth angles, for a certain CDP (Line, Cdp), the anisotropy intensity inverted by step S102 is 0.2, and the azimuth angle is 165 degrees (which can be denoted as -15 degrees). Therefore, the included angles between the six azimuth attribute bodies and the major axis can be calculated as 30 degrees, 60 degrees, 90 degrees, 120 degrees, 150 degrees, and 180 degrees respectively. Taking the attribute body with an azimuth angle of 15 degrees as an example, at this time a = 1, b = 0.2, and through the above standard equation, ρ1 = 0.172 can be solved; in turn, the ρ values of other azimuth attribute bodies can be solved and denoted as (ρ2, ρ3, ρ4, ρ5, ρ6). According to the anisotropy theory, the amplitude difference is the largest when it is perpendicular to the development direction of the azimuth angle, and the maximum azimuth anisotropy value needs to be found. However, due to the 90-degree uncertainty of the AVAZ inversion itself (that is, it is impossible to determine which of the major axis and minor axis directions is correct). Therefore, the magnitudes of two sets of ratio values can be calculated simultaneously. First, take ρmax = MAX(ρ1, ρ2, ρ3, ρ4, ρ5, ρ6), and take ρmin = MIN(ρ1, ρ2, ρ3, ρ4, ρ5, ρ6). Calculate the anisotropy contribution rates of the two several azimuth bodies respectively as: ρ1 / ρmax, ρ2 / ρmax, ρ3 / ρmax, ρ4 / ρmax, ρ5 / ρmax, ρ6 / ρmax; ρ1 / ρmin, ρ2 / ρmin, ρ3 / ρmin, ρ4 / ρmin, ρ5 / ρmin, ρ6 / ρmin. Repeating the previous steps cyclically along the entire three-dimensional inline direction, the azimuth anisotropy contribution rates of all CDP points in the entire three-dimensional body can be obtained.

[0110] In this way, the method of ellipse fitting can be used to calculate the magnitudes of two sets of ratio values simultaneously, and then calculate the anisotropy contribution rates of two types of several azimuth bodies respectively. After cycling through all steps, the entire 3D body line channel direction is cycled and repeated, and the azimuth anisotropy contribution rates of all CDP points in the entire 3D body can be obtained, improving the objectivity, accuracy, and comprehensiveness of the obtained anisotropy contribution rates.

[0111] According to an embodiment of the present invention, in step S104, determining the dimensionality reduction result of the azimuth attribute body according to the anisotropy contribution rate of the azimuth body includes:

[0112] Obtaining the result after fusion of each azimuth attribute according to each azimuth anisotropy contribution rate:

[0113] Obtaining the dimensionality reduction result of the azimuth attribute according to the result after fusion of each azimuth attribute.

[0114] According to Embodiment 1 of the present invention, obtaining the result after fusion of each azimuth attribute according to each azimuth anisotropy contribution rate includes:

[0115] Performing linear superposition on each azimuth attribute body according to each azimuth anisotropy contribution rate to obtain the result after fusion of each azimuth attribute.

[0116] According to an embodiment of the present invention, according to the anisotropy contribution rates of several azimuths in step S103, adding several azimuth attribute bodies linearly can calculate the result after fusion of several azimuth attributes, which can be denoted as

[0117] Mmax = N1 * ρ1 / ρmax + N2 * ρ2 / ρmax + N3 * ρ3 / ρmax + N4 * ρ4 / ρmax +

[0118] N5 * ρ5 / ρmax + N6 * ρ6 / ρmax;

[0119] Or

[0120] Mmin = N1 * ρ1 / ρmin + N2 * ρ2 / ρmin + N3 * ρ3 / ρmin + N4 * ρ4 / ρmin +

[0121] N5 * ρ5 / ρmin + N6 * ρ6 / ρmin;

[0122] According to an embodiment of the present invention, based on the result of fusing the respective azimuth attributes, a dimensionality reduction result of the azimuth attributes is obtained. According to the cognitive result of the full superposition body attributes, the calculated Mmax and Mmin can be compared and analyzed to obtain the correct result. Generally speaking, since the regional tectonic stress directions received in a work area are often the same, there is only one result for the fracture development direction. Therefore, only one of Mmax and Mmin is correct. However, it cannot be excluded that in some individual work areas, both results are the preferred results in some areas. In this case, it is even more necessary to analyze specific problems specifically.

[0123] In this way, the result of fusing the azimuth attributes can be obtained by linear addition, and then the correct result can be obtained through comparative analysis, improving the accuracy and objectivity of the dimensionality reduction result.

[0124] According to the present invention, a five-dimensional seismic trace gather can be obtained by performing all-round processing on seismic data of a "two-wide and one-high" seismic acquisition system. The pre-stack seismic trace gather is stacked according to N different finite azimuth angles to obtain an azimuth stack body, and then attribute calculation is performed to obtain an azimuth attribute body. AVAZ inversion is performed on the five-dimensional seismic trace gather to obtain an anisotropic body and a fracture azimuth body. Based on the concept of ellipticity in elliptic fitting, regression analysis is performed on anisotropy and azimuth to obtain the anisotropy contribution rates of different azimuth attribute bodies and perform linear addition to obtain the dimensionality reduction result of the azimuth attributes. According to the present invention, it is possible to efficiently utilize "two-wide and one-high" seismic data, fully excavate the azimuth angle information in pre-stack seismic data, and then effectively characterize complex geological anomalies such as faults-fractures and river channels. When obtaining a five-dimensional pre-stack gather according to seismic data, the seismic data can be comprehensively processed to obtain a five-dimensional seismic trace gather after all-round processing of the seismic data, providing a more comprehensive data basis for obtaining the dimensionality reduction result. When determining the azimuth attribute body according to the five-dimensional pre-stack gather, three-dimensional high-precision attribute calculation can be carried out to obtain the azimuth attribute body, improving the accuracy of the azimuth attribute body. When determining the anisotropy strength and azimuth angle, fracture inversion can be carried out through various methods to obtain the anisotropy strength and azimuth angle, providing a more convenient data basis for subsequent dimensionality reduction and fusion. When determining the anisotropy contribution rate of the azimuth attribute body, the method of elliptic fitting can be used. By simultaneously calculating the magnitudes of two sets of ratio values, the anisotropy contribution rates of two several azimuth bodies can be calculated respectively. After cycling through all steps and repeating the loop in the entire three-dimensional body line direction, the azimuth anisotropy contribution rates of all CDP points in the entire three-dimensional body can be obtained. The objectivity, accuracy, and comprehensiveness of the obtained anisotropy contribution rate are improved. When determining the dimensionality reduction result, the result of fusing the azimuth attributes can be obtained by linear addition, and then the correct result can be obtained through comparative analysis, improving the accuracy and objectivity of the dimensionality reduction result.

[0125] Example 2

[0126] Figure 5 The system schematic diagram of the azimuth attribute dimensionality reduction method based on ellipse analysis is shown. The system includes:

[0127] A seismic data processing module, which obtains a five-dimensional pre-stack gather according to seismic data;

[0128] An azimuth attribute volume determination module, which determines an azimuth attribute volume according to the five-dimensional pre-stack gather;

[0129] An anisotropy contribution rate determination module, which determines the anisotropy contribution rate of the azimuth attribute volume according to the five-dimensional pre-stack gather;

[0130] A dimensionality reduction result determination module, which determines the dimensionality reduction result of the azimuth attribute volume according to the anisotropy contribution rate of the azimuth volume.

[0131] According to an embodiment of the present invention, the azimuth attribute volume determination module is further configured to obtain a pre-stack azimuth offset seismic gather after migration according to the five-dimensional pre-stack gather;

[0132] Determine a partial azimuth stack volume according to the pre-stack azimuth offset seismic gather after migration;

[0133] Determine the azimuth attribute volume according to the partial azimuth stack volume.

[0134] Determining a partial azimuth stack volume according to the pre-stack azimuth offset seismic gather after migration includes:

[0135] Performing stacking on the pre-stack azimuth offset seismic gather after migration according to a preset partial azimuth angle to obtain the partial azimuth stack volume.

[0136] Determining the azimuth attribute volume according to the partial azimuth stack volume includes:

[0137] Obtaining the azimuth attribute volume according to performing one or more of eigen-coherence attribute calculation, curvature attribute calculation, seismic tensor attribute calculation, edge detection attribute calculation, and ant attribute calculation on the partial azimuth stack volume.

[0138] According to an embodiment of the present invention, the anisotropy contribution rate determination module is further configured to determine the anisotropy strength and azimuth angle according to the five-dimensional pre-stack gather;

[0139] Determine the anisotropy contribution rate of the azimuth attribute volume according to the anisotropy strength and azimuth angle.

[0140] Determining the anisotropy contribution rate of the azimuth attribute volume according to the anisotropy strength and azimuth angle includes:

[0141] Determine the anisotropy intensity as the value of non-ellipticity;

[0142] Determine the major axis value of the ellipse according to the azimuth angle and the value of non-ellipticity;

[0143] Determine the major axis value of the ellipse as the anisotropy value;

[0144] Determine the maximum value of the anisotropy value and the anisotropy value, and determine the anisotropy contribution rate.

[0145] According to an embodiment of the present invention, the dimensionality reduction result determination module is further configured to determine the dimensionality reduction result of the azimuth attribute body according to the anisotropy contribution rate of the azimuth body, including:

[0146] Obtain the result after fusion of each azimuth attribute according to the anisotropy contribution rate of each azimuth:

[0147] Obtain the dimensionality reduction result of the azimuth attribute according to the result after fusion of each azimuth attribute.

[0148] Obtain the result after fusion of each azimuth attribute according to the anisotropy contribution rate of each azimuth, including:

[0149] Perform linear superposition on each azimuth attribute body according to the anisotropy contribution rate of each azimuth to obtain the result after fusion of each azimuth attribute.

[0150] Example 3

[0151] Figure 6 FIG. 800 is a block diagram showing an azimuth attribute dimensionality reduction device 800 based on ellipse analysis according to an embodiment of the present disclosure. For example, the data transmission device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or other terminal devices. The data transmission device includes a memory 804, a processor 802, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned Netty-based large file transmission and breakpoint resume method is implemented.

[0152] The data transmission device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output interface 812, a sensor component 814, and a communication component 816.

[0153] The processing component 802 generally controls the overall operation of the data transmission device 800, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-described methods. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0154] The memory 804 is configured to store various types of data to support the operation of the data transmission device 800. Examples of such data include instructions for any application or method operating on the data transmission device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disks, or optical disks.

[0155] The power component 806 provides power to the various components of the data transmission device 800. The power component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the data transmission device 800.

[0156] The multimedia component 808 includes a screen that provides an output interface between the data transmission device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the edges of the touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operations. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the data transmission device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each of the front camera and the rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0157] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when the data transmission device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0158] The input / output interface 812 provides an interface between the processing component 802 and a peripheral interface module, and the peripheral interface module can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0159] The sensor component 814 includes one or more sensors for providing status assessments of various aspects of the data transmission device 800. For example, the sensor component 814 can detect the on / off state of the data transmission device 800, the relative positioning of components, such as the display and keypad of the data transmission device 800. The sensor component 814 can also detect a change in the position of the data transmission device 800 or a component of the data transmission device 800, the presence or absence of user contact with the data transmission device 800, the orientation or acceleration / deceleration of the data transmission device 800, and the temperature change of the data transmission device 800. The sensor component 814 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 814 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0160] The communication component 816 is configured to facilitate communication between the data transmission device 800 and other devices in a wired or wireless manner. The data transmission device 800 can access a wireless network based on communication standards, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0161] In an exemplary embodiment, the data transmission device 800 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for performing the above method.

[0162] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, and the above computer program instructions can be executed by a processor 820 of the data transmission device 800 to complete the above method.

[0163] Example 4

[0164] Figure 7 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic storage medium 1900 can be provided as a server or a terminal. The electronic storage medium 1900 includes a processing unit 1922, which further includes one or more processors, and memory resources represented by a storage unit 1932 for storing instructions executable by the processing unit 1922, such as application programs. The application programs stored in the storage unit 1932 can include one or more modules each corresponding to a set of instructions. In addition, the processing unit 1922 is configured to execute instructions to perform the above method.

[0165] The electronic storage medium 1900 may further include a power supply unit 1926 configured to perform power management of the electronic storage medium 1900, a wired or wireless network interface 1950 configured to connect the electronic storage medium 1900 to a network, and an input / output interface 1958. The electronic storage medium 1900 can operate based on an operating system stored in the storage unit 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

[0166] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a storage unit 1932 including computer program instructions, and the above computer program instructions can be executed by a processing unit 1922 of the electronic storage medium 1900 to complete the above method.

[0167] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0168] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed to be a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0169] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0170] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via an Internet service provider through the Internet). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0171] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer - readable program instructions.

[0172] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine such that the instructions, when executed by the processor of the computer or other programmable data - processing apparatus, create a means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner, so that the computer - readable medium storing the instructions comprises a manufacture, which includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0173] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to generate a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0174] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the boxes may occur out of the order noted in the figures. For example, two consecutive boxes may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each box of the block diagrams and / or flowcharts, and combinations of boxes in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0175] The computer program product may be implemented specifically by hardware, software, or a combination thereof. In an alternative embodiment, the computer program product is embodied as a computer storage medium. In another alternative embodiment, the computer program product is embodied as a software product, such as a Software Development Kit (SDK), etc.

[0176] It can be understood that the above-mentioned embodiments mentioned in the present disclosure can be combined with each other to form combined embodiments without violating the principle logic. Due to space limitations, the present disclosure will not elaborate further. Those skilled in the art can understand that in the above method of the specific implementation manner, the specific execution order of each step should be determined according to its function and possible internal logic.

[0177] Note that, unless otherwise directly stated, all features disclosed in this specification (including any appended claims, abstract, and drawings) may be replaced by alternative features serving the same, equivalent, or similar purposes. Therefore, unless otherwise explicitly stated, each feature disclosed is only an example of a group of equivalent or similar features. Where used, "furthermore", "preferably", "moreover", and "even more preferably" are simple introductions for elaborating another embodiment based on the foregoing embodiments. The content following the "furthermore", "preferably", "moreover", or "even more preferably" in combination with the foregoing embodiments constitutes a complete composition of another embodiment. Combinations can be arbitrarily made among several "furthermore", "preferably", "moreover", or "even more preferably" settings following the same embodiment to form yet another embodiment.

[0178] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Without departing from the said principles, the embodiments of the present invention may be subject to any deformation or modification.

[0179] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present disclosure, and not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for dimension reduction of azimuth attributes based on ellipse analysis, characterized in that, it includes: Obtain a five-dimensional pre-stack gather according to seismic data; Determine an azimuth attribute volume according to the five-dimensional pre-stack gather; Determine the anisotropic contribution rate of the azimuth attribute volume according to the five-dimensional pre-stack gather; Determine the dimension reduction result of the azimuth attribute volume according to the anisotropic contribution rate of the azimuth volume.

2. The method for dimension reduction of azimuth attributes based on ellipse analysis according to claim 1, characterized in that, Determining an azimuth attribute volume according to the five-dimensional pre-stack gather includes: Obtain a pre-stack azimuth offset seismic gather after migration according to the five-dimensional pre-stack gather; Determine a partial azimuth stack volume according to the pre-stack azimuth offset seismic gather after migration; Determine the azimuth attribute volume according to the partial azimuth stack volume.

3. The method for dimension reduction of azimuth attributes based on ellipse analysis according to claim 2, characterized in that, Determining a partial azimuth stack volume according to the pre-stack azimuth offset seismic gather after migration includes: Stack the pre-stack azimuth offset seismic gather after migration according to a preset partial azimuth angle to obtain the partial azimuth stack volume.

4. The method for dimension reduction of azimuth attributes based on ellipse analysis according to claim 2, characterized in that, Determining the azimuth attribute volume according to the partial azimuth stack volume includes: Obtain the azimuth attribute volume by performing one or more of eigen-coherence attribute calculation, curvature attribute calculation, seismic tensor attribute calculation, edge detection attribute calculation, and ant attribute calculation on the partial azimuth stack volume.

5. The method for dimension reduction of azimuth attributes based on ellipse analysis according to claim 1, characterized in that, Determining the anisotropic contribution rate of the azimuth attribute volume according to the five-dimensional pre-stack gather includes: Determine the anisotropic intensity and azimuth angle according to the five-dimensional pre-stack gather; Determine the anisotropic contribution rate of the azimuth attribute volume according to the anisotropic intensity and azimuth angle.

6. The method for dimension reduction of azimuth attributes based on ellipse analysis according to claim 5, characterized in that, Determining the anisotropic contribution rate of the azimuth attribute volume according to the anisotropic intensity and azimuth angle includes: Determine the value of non-ellipticity as the anisotropic intensity; Determine the major axis value of the ellipse according to the azimuth angle and the value of non-ellipticity; Determine the major axis value of the ellipse as the anisotropic value; Determine the maximum value of the anisotropic values, and determine the anisotropic contribution rate according to the anisotropic values.

7. The method for dimension reduction of azimuth attributes based on ellipse analysis according to claim 1, characterized in that, Determining the dimension reduction result of the azimuth attribute volume according to the anisotropic contribution rate of the azimuth volume includes: Obtain the result after fusion of each azimuth attribute according to the anisotropic contribution rate of each azimuth: Obtain the dimension reduction result of the azimuth attribute according to the result after fusion of each azimuth attribute.

8. The method for dimension reduction of azimuth attributes based on ellipse analysis according to claim 7, characterized in that, Obtaining the result after fusion of each azimuth attribute according to the anisotropic contribution rate of each azimuth includes: According to the contribution rate of anisotropy in each azimuth, perform linear superposition on each azimuth attribute volume to obtain the result after fusion of each azimuth attribute.

9. An azimuth attribute dimensionality reduction system based on elliptical analysis, characterized in that, it includes: A seismic data processing module, which obtains a five-dimensional pre-stack gather according to seismic data; An azimuth attribute volume determination module, which determines an azimuth attribute volume according to the five-dimensional pre-stack gather; An anisotropy contribution rate determination module, which determines the anisotropy contribution rate of the azimuth attribute volume according to the five-dimensional pre-stack gather; A dimensionality reduction result determination module, which determines the dimensionality reduction result of the azimuth attribute volume according to the anisotropy contribution rate of the azimuth volume.

10. An azimuth attribute dimensionality reduction device based on elliptical analysis, including: A processor; A memory for storing instructions executable by the processor; wherein, the processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1-8.

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