An azimuth attribute dimension reduction method and device, a storage medium and an electronic device

Abstract

The present application relates to the field of seismic exploration technology, in particular to a kind of azimuth attribute dimension reduction method, device, storage medium and electronic equipment, method comprising: the attribute calculation of the azimuth stack of pre-set quantity is carried out respectively, to obtain the azimuth attribute volume corresponding to each azimuth stack;The seismic trace set is inverted, to obtain anisotropy intensity and inversion azimuth;Based on anisotropy intensity and inversion azimuth, each azimuth attribute volume is analyzed to obtain the azimuth contribution rate of each azimuth attribute volume;The linear addition of pre-set quantity of azimuth contribution rate is carried out, to obtain the dimension reduction result of azimuth attribute;It can realize the dimension reduction optimization fusion of multiple azimuth attribute volumes, to face " two wide one high " seismic data and carry out efficient use, fully excavate azimuth angle information in prestack seismic data, and then realize the effective characterization of fracture-fracture, river channel and other complex geological anomaly body.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, and in particular to a method, apparatus, storage medium, and electronic device for dimensionality reduction of azimuth attributes based on pre-stack gathers. Background Technology

[0002] With the increasing availability of seismic data based on the "two-wide-one-high" acquisition technology, and after preprocessing the seismic data, five-dimensional pre-stack seismic gathers can be obtained, hereinafter referred to as five-dimensional pre-stack gathers. Based on the five-dimensional pre-stack gathers, azimuth stacking or offset stacking can be performed to obtain azimuth stacks or offset stacks.

[0003] By performing attribute calculations on the azimuth superposition volume or the offset superposition volume, we can obtain the azimuth attribute volume or the offset attribute volume. Since the azimuth attribute volume has multiple data volumes, how to reduce the dimensionality of multiple azimuth attribute volumes is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] This invention provides a method, apparatus, storage medium, and electronic device for dimensionality reduction of orientation attributes, which solves the technical problem of difficulty in dimensionality reduction of multiple orientation attribute volumes.

[0005] In a first aspect, the present invention provides a method for dimensionality reduction of orientation attributes, comprising:

[0006] Attribute calculations are performed on a preset number of directional superimposed objects to obtain the directional attribute object corresponding to each directional superimposed object;

[0007] Inversion is performed on the seismic gathers to obtain the anisotropy intensity and inversion azimuth.

[0008] Elliptical analysis is performed on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume.

[0009] The directional contribution rates of a preset number of locations are linearly summed to obtain the dimensionality reduction result of the directional attributes.

[0010] In some embodiments, before the step of performing attribute calculations on a preset number of azimuth superimposed volumes to obtain the azimuth attribute volume corresponding to each azimuth superimposed volume, the method further includes:

[0011] The seismic gathers are azimuthally superimposed to generate a preset number of azimuth superimposed volumes.

[0012] In some embodiments, prior to the step of azimuth stacking of seismic gathers, the method further includes:

[0013] Seismic data is preprocessed to generate seismic gathers;

[0014] The preprocessing includes one or more of the following: trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction.

[0015] Seismic data is obtained through omnidirectional migration imaging, and the seismic data includes azimuth angles.

[0016] In some embodiments, in the step of calculating the attributes of a preset number of azimuth superimposed volumes, the attributes include one or more of the following: intrinsic coherence attributes, curvature attributes, seismic tensor attributes, edge detection attributes, and ant attributes.

[0017] In some embodiments, in the step of performing elliptic analysis on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume, the elliptic analysis includes:

[0018] Using the anisotropy intensity as the ellipticity, and combining the inverted azimuth angle with the azimuth angle of each azimuth attribute volume, elliptic analysis is performed on the corresponding azimuth attribute volume to obtain the corresponding axis length of each azimuth attribute volume.

[0019] Based on the corresponding axis length of each directional attribute body, calculate the directional contribution rate of each directional attribute body.

[0020] In some embodiments, the step of calculating the azimuth contribution rate of each azimuth attribute volume based on the corresponding axis length of each azimuth attribute volume includes:

[0021] Obtain the maximum and minimum axis lengths from the set of axis lengths corresponding to each orientation attribute body;

[0022] The ratio of the corresponding axis length of each directional attribute body to the maximum or minimum axis length is taken as the directional contribution rate of the corresponding directional attribute body.

[0023] In some embodiments, the step of linearly summing a preset number of azimuth contribution rates to obtain a dimensionality reduction result of the azimuth attribute includes:

[0024] The attribute value of each directional attribute is multiplied by the directional contribution rate and then summed to obtain the dimensionality reduction result of the directional attribute.

[0025] Secondly, the present invention provides a location attribute dimensionality reduction device, comprising:

[0026] The attribute calculation module is used to perform attribute calculations on a preset number of directional superimposed bodies to obtain the directional attribute body corresponding to each directional superimposed body.

[0027] The inversion module is used to invert seismic gathers to obtain anisotropy intensity and inversion azimuth.

[0028] The ellipse analysis module is used to perform ellipse analysis on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume.

[0029] The dimensionality reduction module is used to linearly add up a preset number of directional contribution rates to obtain the dimensionality reduction result of the directional attributes.

[0030] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of the first aspect.

[0031] Fourthly, the present invention provides an electronic device including a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the method of the first aspect.

[0032] This invention provides a method, apparatus, storage medium, and electronic device for azimuth attribute dimensionality reduction. It calculates attributes for a preset number of azimuth stacks to obtain the azimuth attribute volume for each stack; inverts seismic gathers to obtain anisotropy intensity and inversion azimuth; performs elliptic analysis on each azimuth attribute volume based on anisotropy intensity and inversion azimuth to obtain the azimuth contribution rate of each volume; and linearly adds the preset number of azimuth contribution rates to obtain the dimensionality reduction result of the azimuth attribute. This enables dimensionality reduction optimization and fusion of multiple azimuth attribute volumes, thereby facilitating efficient utilization of "two-wide-one-high" seismic data, fully mining the azimuth information in pre-stack seismic data, and ultimately achieving effective characterization of complex geological anomalies such as faults-fissures and river channels. Attached Figure Description

[0033] The invention will now be described in more detail with reference to embodiments and the accompanying drawings:

[0034] Figure 1 This is a schematic diagram of a dimensionality reduction method for orientation attributes provided in an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of a dimensionality reduction method for orientation attributes provided in an embodiment of the present invention;

[0036] Figure 3a A schematic diagram of a layer-by-layer slice of the property coherence body at an azimuth angle of 15 degrees in a certain seismic work area, provided in an embodiment of the present invention;

[0037] Figure 3b A schematic diagram of a layer-by-layer slice of the property coherence body at an azimuth angle of 45 degrees in a certain seismic work area, provided in an embodiment of the present invention;

[0038] Figure 3cA schematic diagram of a layer-by-layer slice of the property coherence body at an azimuth angle of 75 degrees in a certain seismic work area, provided in an embodiment of the present invention;

[0039] Figure 3d A schematic diagram of a layer-by-layer slice of the property coherence body at an azimuth angle of 105 degrees in a seismic work area provided in an embodiment of the present invention;

[0040] Figure 3e A schematic diagram of a layer-by-layer slice of the property coherence body at an azimuth angle of 135 degrees in a seismic work area provided in an embodiment of the present invention;

[0041] Figure 3f A schematic diagram of a layer-by-layer slice of the property coherence body at an azimuth angle of 165 degrees in a seismic work area, provided as an embodiment of the present invention.

[0042] Figure 4 A schematic diagram of a coherent layer-by-layer slice of a post-stack seismic profile provided in an embodiment of the present invention;

[0043] Figure 5 This is a schematic diagram of a volumetric slice along a layer for azimuth attribute dimensionality reduction based on elliptic analysis, provided in an embodiment of the present invention.

[0044] Figure 6 This is a schematic diagram of a directional attribute dimensionality reduction device provided in an embodiment of the present invention.

[0045] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present invention and to fully understand and implement the process of how the present invention uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The embodiments of the present invention and the various features therein can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0047] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0048] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0049] With the increasing availability of seismic data based on the "two-wide-one-high" acquisition technology, and after preprocessing of the seismic data, we can obtain pre-stack seismic gathers in five-dimensional (three-dimensional space XYZ, azimuth, and offset) space, hereinafter referred to as five-dimensional pre-stack gathers. By performing azimuth stacking or offset stacking based on the five-dimensional pre-stack gathers, we can obtain azimuth stacks or offset stacks.

[0050] By performing attribute calculations on the azimuth superposition volume or the offset superposition volume, we can obtain the azimuth attribute volume or the offset attribute volume. Since the azimuth attribute volume has multiple data volumes, how to reduce the dimensionality of multiple azimuth attribute volumes is a technical problem that urgently needs to be solved in this field.

[0051] In some cases, dimensionality reduction of directional attribute volumes is often achieved by directly stacking attributes or fusing RGB three colors. However, these dimensionality reduction methods have low accuracy, the mixed images interfere with each other, and RGB fusion is only a visual fusion at the display level, not a true attribute fusion dimensionality reduction method.

[0052] Therefore, there is an urgent need in this field for a dimensionality reduction optimization technique for azimuth attribute volumes, and ultimately to achieve dimensionality reduction optimization and fusion of multiple azimuth attribute volumes, so as to efficiently utilize "two-width and one-height" seismic data, fully explore the azimuth information in pre-stack seismic data, and thus achieve effective characterization of complex geological anomalies such as faults-fissures and river channels.

[0053] To address this issue, this invention proposes a seismic exploration technique, specifically a dimensionality reduction technique based on azimuth attribute superposition volume derived from elliptic analysis regression algorithm. The technical solution of this invention will be described below with reference to specific embodiments.

[0054] Example 1

[0055] Figure 1 This is a flowchart of a dimensionality reduction method for orientation attributes according to an embodiment of the present invention. Figure 1 As shown, this embodiment provides a method for dimensionality reduction of orientation attributes, including:

[0056] Attribute calculations are performed on a preset number of directional superimposed objects to obtain the directional attribute object corresponding to each directional superimposed object;

[0057] Inversion is performed on the seismic gathers to obtain the anisotropy intensity and inversion azimuth.

[0058] Elliptical analysis is performed on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume.

[0059] The directional contribution rates of a preset number of locations are linearly summed to obtain the dimensionality reduction result of the directional attributes.

[0060] In the technical solution of this embodiment, the five-dimensional pre-stack gathers are stacked according to N different finite azimuth angles (the number of azimuth angles N is, for example, between 3 and 8; the more azimuth angles N there are, the greater the computational load) to obtain an azimuth stack. The preset number is, for example, the number of azimuth angles N.

[0061] The technical solution of this embodiment calculates the attributes of a preset number of azimuth stacks to obtain the azimuth attribute volume corresponding to each azimuth stack; it inverts the seismic gathers to obtain the anisotropy intensity and inversion azimuth; it performs elliptic analysis on each azimuth attribute volume based on the anisotropy intensity and inversion azimuth to obtain the azimuth contribution rate of each azimuth attribute volume; and it linearly adds the preset number of azimuth contribution rates to obtain the dimensionality reduction result of the azimuth attributes. This enables the dimensionality reduction optimization and fusion of multiple azimuth attribute volumes, thereby enabling efficient utilization of "two-wide and one-high" seismic data, fully mining the azimuth information in pre-stack seismic data, and thus achieving effective characterization of complex geological anomalies such as faults-fissures and river channels.

[0062] Example 2

[0063] Based on the above embodiments, this embodiment provides a method for directional attribute dimensionality reduction. Before the step of calculating the attributes of a preset number of directional superimposed volumes to obtain the directional attribute volume corresponding to each directional superimposed volume, the method further includes:

[0064] The seismic gathers are azimuthally superimposed to generate a preset number of azimuth superimposed volumes.

[0065] In some implementations, the method further includes, prior to the step of azimuth stacking of the seismic gathers:

[0066] Seismic data is preprocessed to generate seismic gathers;

[0067] The preprocessing includes one or more of the following: trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction.

[0068] Seismic data is obtained through omnidirectional migration imaging, and the seismic data includes azimuth angles.

[0069] In some implementations, the step of calculating the attributes of a preset number of azimuth superimposed volumes includes one or more of the following: intrinsic coherence attributes, curvature attributes, seismic tensor attributes, edge detection attributes, and ant attributes.

[0070] In the technical solution of this embodiment, a five-dimensional pre-stack gather can be obtained by performing comprehensive processing of seismic data on a "two-width and one-height" seismic acquisition system. The five dimensions include the three-dimensional XYZ, azimuth, and offset.

[0071] The five-dimensional pre-stack gathers are stacked according to N different finite azimuth angles (the number of azimuth angles N is, for example, between 3 and 8; the more azimuth angles N, the greater the computational load) to obtain an azimuth stack. In this embodiment, the technical solution only performs attribute dimensionality reduction on the azimuth stack, and does not perform dimensionality reduction on the offset stack.

[0072] The attributes of the directional superposition volume are calculated to obtain the directional attribute volume.

[0073] AVAZ inversion was performed on the five-dimensional pre-stack gather to obtain the anisotropic (crack intensity) volume and the crack orientation volume.

[0074] Based on the concept of ellipticity in elliptic fitting, regression analysis is performed on anisotropy and orientation to obtain the anisotropy contribution rate of different orientation attribute volumes.

[0075] By linearly summing the anisotropic contribution rates of different orientation attribute volumes, the dimensionality reduction result of the orientation attribute can be obtained.

[0076] The specific steps are as follows: a five-dimensional pre-stack gather is generated after processing; an azimuth stack is generated by finite azimuth stacking; an azimuth attribute volume is formed by attribute calculation; an anisotropic volume and an azimuth volume are obtained by crack inversion; an ellipse analysis is used to obtain the azimuth contribution rate; and the azimuth cumulative amount is linearly added to obtain the azimuth dimensionality reduction volume.

[0077] Step 1: Preprocess the seismic data to generate a five-dimensional pre-stack gather;

[0078] Preprocessing is performed on "two-width and one-height" seismic data. This preprocessing typically includes, but is not limited to, trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction. In this embodiment, the formation of the five-dimensional pre-stack gather requires omnidirectional migration imaging while preserving azimuth and offset.

[0079] Step 2: Perform finite azimuth stacking on the offset front azimuth offset (azimuth incident angle) seismic gathers to generate an azimuth stack;

[0080] For seismic gathers with offset front-stack offset (azimuth incident angle), multiple azimuth stacks are formed by stacking partial azimuth angles. In the example of this invention, a total of 6 azimuth stacks are formed, namely: first azimuth stack, second azimuth stack, third azimuth stack, fourth azimuth stack, fifth azimuth stack, and sixth azimuth stack.

[0081] The first azimuth superposition is superimposed according to azimuth angles of 1 degree to 30 degrees, with azimuth angle Azi1 denoted as 15 degrees;

[0082] The second azimuth superposition is superimposed according to azimuth angles of 31 degrees to 60 degrees, with azimuth angle Azi2 denoted as 45 degrees;

[0083] The third-order superposition body is superimposed according to the azimuth angle of 61 degrees to 90 degrees, and the azimuth angle Azi3 is recorded as 75 degrees;

[0084] The fourth azimuth superposition is superimposed according to azimuth angles of 91 degrees to 120 degrees, with azimuth angle Azi4 denoted as 115 degrees;

[0085] The fifth azimuth superposition body is superimposed with azimuth angles of 121 degrees to 150 degrees, and the azimuth angle Azi5 is 135 degrees;

[0086] The sixth azimuth superposition is formed by superimposing azimuth angles from 151 degrees to 180 degrees, with azimuth angle Azi6 denoted as 165 degrees.

[0087] Step 3: Perform attribute calculations based on each directional superposition to form a directional attribute body corresponding to each directional superposition.

[0088] Each azimuth stack obtained through finite azimuth stacking has the same data as a conventional post-stack 3D data volume. Therefore, high-precision 3D attribute calculations can be performed based on each azimuth stack, such as calculating one or more of the following attributes: intrinsic coherence, curvature, seismic tensor, edge detection, ants, etc. In the example of this invention, six azimuth attribute volumes are formed based on the above six azimuth stacks.

[0089] Step 4: Crack inversion yields anisotropic and azimuthal volumes, i.e., anisotropic intensity and inversion azimuth angle φ.

[0090] For the five-dimensional pre-stack gathers processed by omnidirectional migration imaging, crack inversion is performed to obtain anisotropic intensity and azimuth. In this embodiment, crack inversion is, for example, AVAZ inversion, and crack parameters are obtained based on AVAZ inversion.

[0091] The technical solution of this embodiment generates a five-dimensional pre-stack gather and retains the azimuth angle in the seismic data to obtain an azimuth stack volume, and further obtains an azimuth attribute volume through attribute calculation.

[0092] Example 3

[0093] Based on the above embodiments, this embodiment provides a method for azimuth attribute dimensionality reduction. In the step of performing elliptic analysis on each azimuth attribute volume based on anisotropy intensity and inverted azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume, the elliptic analysis includes:

[0094] Using the anisotropy intensity as the ellipticity, and combining the inverted azimuth angle with the azimuth angle of each azimuth attribute volume, elliptic analysis is performed on the corresponding azimuth attribute volume to obtain the corresponding axis length of each azimuth attribute volume.

[0095] Based on the corresponding axis length of each directional attribute body, calculate the directional contribution rate of each directional attribute body.

[0096] In some implementations, the step of calculating the azimuth contribution rate of each azimuth attribute volume based on the corresponding axis length of each azimuth attribute volume includes:

[0097] Obtain the maximum and minimum axis lengths from the set of axis lengths corresponding to each orientation attribute body;

[0098] The ratio of the corresponding axis length of each directional attribute body to the maximum or minimum axis length is taken as the directional contribution rate of the corresponding directional attribute body.

[0099] Step 5: Ellipse analysis yields the azimuth contribution rate.

[0100] Early anisotropic crack parameter inversion was based on elliptic fitting, where crack strength (i.e., anisotropic strength) is the ellipticity, defined as the ratio of minor axis to major axis, and the definition of azimuth φ is consistent with the inversion azimuth φ in step four.

[0101] The technical solution of this embodiment draws on the concept of ellipticity, and the anisotropic intensity obtained in step four is recorded as the value of ellipticity.

[0102] For ease of understanding, in such Figure 2 In the ellipse shown, the length of the major axis is a; the length of the minor axis is b; and the ellipticity η = b / a.

[0103] The azimuth angle of the ellipse in coordinate system MON is φ. For a certain azimuth angle Azi (Azi1, Azi2, Azi3, Azi4, Azi5, Azi6), Azi is... Figure 2 The angle between the azimuth angle Azi and the midpoint MOP. The axis length OP of this azimuth angle Azi is denoted as ρ.

[0104] For a given azimuth property, its azimuth angle Azi is the angle between MOP and MOP, i.e., Azi = θ + φ; the angle between OP and the major axis a of the standard ellipse is θ = Azi - φ.

[0105] Here, point O is any one of the six directional attribute volumes. According to the standard equation of an ellipse, point O in the xOy coordinate system has the following relationship with its x and y coordinates:

[0106]

[0107] Since the axis length of point O is ρ, from Figure 2 As can be seen from this, x = ρcosθ and y = ρsinθ. Let the value of the major axis a be 1, and by substituting a and b into the above standard equation using the value of the anisotropic strength b, we can obtain the value of the axis length ρ.

[0108] In this embodiment, there are a total of six azimuth angles Azi. For a certain CDP (Line, Cdp), the anisotropy intensity obtained from the inversion in step four is 0.25, and the inverted azimuth angle is 15 degrees (here, the azimuth of the major axis is 15 degrees, i.e., φ = 15). Therefore, the angles θ = Azi - φ between the six azimuth attribute volumes and the major axis of the ellipse can be calculated as 0 degrees, 30 degrees, 60 degrees, 90 degrees, 120 degrees, and 150 degrees, respectively. (Note: For ease of calculation, the angle θ is intentionally set to an integer. Of course, this division of azimuth angles is also quite common in daily use.)

[0109] Taking the azimuth attribute volume with an azimuth angle of 75 degrees (i.e., the third azimuth attribute volume) as an example, the anisotropy intensity of the inversion in step four is 0.25. At this time, a = 1 and b = 0.25. Through the above standard equation, we can solve for ρ3 = 0.285.

[0110] The specific calculation method is as follows: Substitute a = 1, b = 0.25, and θ = 60 degrees into the equation, and we get:

[0111]

[0112] Using trigonometric functions, we know that cos60° = 0.5 and sin60° = 0.866, and thus ρ3 = 0.285.

[0113] The ρ values ​​of the other directional attribute bodies can be obtained sequentially, denoted as (ρ1, ρ2, ρ3, ρ4, ρ5, ρ6). From the trigonometric functions above, we know that the values ​​of ρ1 to ρ6 are between (0.25, 1).

[0114] According to anisotropy theory, the amplitude difference is greatest when it is perpendicular to the azimuth development direction. Therefore, it is necessary to find the maximum azimuth anisotropy value. However, due to the 90-degree uncertainty inherent in AVAZ inversion (i.e., it is impossible to determine which direction, the major or minor axis, is correct), we can simultaneously calculate the magnitudes of two sets of scale values. First, we take ρmax = MAX(ρ1, ρ2, ρ3, ρ4, ρ5, ρ6) and ρmin = MIN(ρ1, ρ2, ρ3, ρ4, ρ5, ρ6).

[0115] Calculate the anisotropy contribution rate of the two six-position bodies respectively:

[0116] ρ1 / ρmax, ρ2 / ρmax, ρ3 / ρmax, ρ4 / ρmax, ρ5 / ρmax, ρ6 / ρmax; and

[0117] ρ1 / ρmin, ρ2 / ρmin, ρ3 / ρmin, ρ4 / ρmin, ρ5 / ρmin, ρ6 / ρmin,

[0118] By repeating the above steps along the entire 3D volume's line direction, the orientational anisotropy contribution rate of all CDP points in the entire 3D volume can be obtained.

[0119] The technical solution of this embodiment is based on the idea of ​​elliptic regression, which calculates the anisotropic contribution rate of each directional attribute volume, and realizes the calculation of the anisotropic contribution rate of each directional attribute volume.

[0120] Example 4

[0121] Based on the above embodiments, this embodiment provides a method for dimensionality reduction of azimuth attributes, which involves linearly adding a preset number of azimuth contribution rates to obtain the dimensionality reduction result of the azimuth attributes, including:

[0122] The attribute value of each directional attribute is multiplied by the directional contribution rate and then summed to obtain the dimensionality reduction result of the directional attribute.

[0123] Step Six: Dimensionality Reduction of N Directional Overlay Attributes

[0124] Based on the anisotropic contribution rates of the six directions obtained in step five, the result of the fusion of the six directional attributes can be calculated by linearly adding the six directional attribute volumes, which can be denoted as:

[0125] Mmax=N1*ρ1 / ρmax+N2*ρ2 / ρmax+N3*ρ3 / ρmax+N4*ρ4 / ρmax+N5*ρ5 / ρmax+N6*ρ6 / ρmax;

[0126] or

[0127] Mmin=N1*ρ1 / ρmin+N2*ρ2 / ρmin+N3*ρ3 / ρmin+N4*ρ4 / ρmin+N5*ρ5 / ρmin+N6*ρ6 / ρmin;

[0128] Where N1 to N6 are the values ​​of the three-dimensional volume attributes.

[0129] Figures 3a to 3f The only shown is a slice of the 3D volume along the layers. The actual calculation should use the overall attribute volume in the 3D work area. Figure 4 This is a coherent slice along the layers of a post-stack seismic profile; Figure 5 Orientation attribute dimensionality reduction volume slices along layers based on elliptic analysis. Figure 5 and Figure 4 The comparison reveals that the azimuth coherence attribute dimensionality reduction technique based on elliptic analysis is significantly superior to the coherence attribute of single post-stack data.

[0130] Following the example in the previous steps, the value range of ρ1 to ρ6 is (0.25, 1), so one of the coefficients must be equal to 1, such as ρ6 / ρmax or ρ1 / ρmin = 1. This is consistent with the development of anisotropy, because all attribute results are relative. What we need is to find the relative maximum or minimum contribution rate in different directional attributes based on the intensity and size of anisotropy, so as to achieve dimensionality reduction (i.e., relative attribute enhancement). Therefore, it is permissible for the attribute value range after dimensionality reduction to differ from the value range of N1 to N6.

[0131] Based on the understanding of the properties of the superimposed body, the calculated Mmax and Mmin can be compared and analyzed to obtain the correct result. In most cases, since the regional tectonic stress direction is often consistent in a work area, there is only one possible crack development direction, so only one of the Mmax and Mmin results is correct.

[0132] The technical solution of this embodiment completes the dimensionality reduction of the attributes by comparing and analyzing the calculated Mmax and Mmin based on the cognitive results of the attributes of the superposition.

[0133] Example 5

[0134] Figure 6 This is a schematic diagram of a location attribute dimensionality reduction device provided in an embodiment of the present invention. Figure 6 As shown, based on the above embodiments, this embodiment provides a location attribute dimensionality reduction device, including:

[0135] The attribute calculation module is used to perform attribute calculations on a preset number of directional superimposed bodies to obtain the directional attribute body corresponding to each directional superimposed body.

[0136] The inversion module is used to invert seismic gathers to obtain anisotropy intensity and inversion azimuth.

[0137] The ellipse analysis module is used to perform ellipse analysis on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume.

[0138] The dimensionality reduction module is used to linearly add up a preset number of directional contribution rates to obtain the dimensionality reduction result of the directional attributes.

[0139] In some embodiments, before the step of performing attribute calculations on a preset number of azimuth superimposed volumes to obtain the azimuth attribute volume corresponding to each azimuth superimposed volume, the method further includes:

[0140] The seismic gathers are azimuthally superimposed to generate a preset number of azimuth superimposed volumes.

[0141] In some embodiments, prior to the step of azimuth stacking of seismic gathers, the method further includes:

[0142] Seismic data is preprocessed to generate seismic gathers;

[0143] The preprocessing includes one or more of the following: trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction.

[0144] Seismic data is obtained through omnidirectional migration imaging, and the seismic data includes azimuth angles.

[0145] In some embodiments, in the step of calculating the attributes of a preset number of azimuth superimposed volumes, the attributes include one or more of the following: intrinsic coherence attributes, curvature attributes, seismic tensor attributes, edge detection attributes, and ant attributes.

[0146] In some embodiments, in the step of performing elliptic analysis on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume, the elliptic analysis includes:

[0147] Using the anisotropy intensity as the ellipticity, and combining the inverted azimuth angle with the azimuth angle of each azimuth attribute volume, elliptic analysis is performed on the corresponding azimuth attribute volume to obtain the corresponding axis length of each azimuth attribute volume.

[0148] Based on the corresponding axis length of each directional attribute body, calculate the directional contribution rate of each directional attribute body.

[0149] In some embodiments, the step of calculating the azimuth contribution rate of each azimuth attribute volume based on the corresponding axis length of each azimuth attribute volume includes:

[0150] Obtain the maximum and minimum axis lengths from the set of axis lengths corresponding to each orientation attribute body;

[0151] The ratio of the corresponding axis length of each directional attribute body to the maximum or minimum axis length is taken as the directional contribution rate of the corresponding directional attribute body.

[0152] In some embodiments, the step of linearly summing a preset number of azimuth contribution rates to obtain a dimensionality reduction result of the azimuth attribute includes:

[0153] The attribute value of each directional attribute is multiplied by the directional contribution rate and then summed to obtain the dimensionality reduction result of the directional attribute.

[0154] The technical solution of this embodiment calculates the attributes of a preset number of azimuth stacks to obtain the azimuth attribute volume corresponding to each azimuth stack; it inverts the seismic gathers to obtain the anisotropy intensity and inversion azimuth; it performs elliptic analysis on each azimuth attribute volume based on the anisotropy intensity and inversion azimuth to obtain the azimuth contribution rate of each azimuth attribute volume; and it linearly adds the preset number of azimuth contribution rates to obtain the dimensionality reduction result of the azimuth attributes. This enables the dimensionality reduction optimization and fusion of multiple azimuth attribute volumes, thereby enabling efficient utilization of "two-wide and one-high" seismic data, fully mining the azimuth information in pre-stack seismic data, and thus achieving effective characterization of complex geological anomalies such as faults-fissures and river channels.

[0155] Other technical features and effects of this embodiment are the same as those of the other embodiments described above, and will not be repeated here.

[0156] Example 6

[0157] Based on the above embodiments, this embodiment provides an application example.

[0158] By performing comprehensive processing of seismic data from a "two-width and one-height" seismic acquisition system, a five-dimensional pre-stack gather can be obtained. The five dimensions include the three-dimensional XYZ, azimuth, and offset.

[0159] The five-dimensional pre-stack gathers are stacked according to N different finite azimuth angles (the number of azimuth angles N is, for example, between 3 and 8; the more azimuth angles N, the greater the computational load) to obtain the azimuth stack. (It should be noted that the technical solution of this invention only performs attribute dimensionality reduction on the azimuth stack, and does not perform dimensionality reduction operation on the offset stack).

[0160] The attributes of the directional superposition volume are calculated to obtain the directional attribute volume.

[0161] AVAZ inversion was performed on the five-dimensional pre-stack gather to obtain the anisotropic (crack intensity) volume and the crack orientation volume.

[0162] Based on the concept of ellipticity in elliptic fitting, regression analysis is performed on anisotropy and orientation to obtain the anisotropy contribution rate of different orientation attribute volumes.

[0163] By linearly summing the anisotropic contribution rates of different orientation attribute volumes, the dimensionality reduction result of the orientation attribute can be obtained.

[0164] The core of the technical solution of this invention is: based on the idea of ​​elliptic regression, the anisotropic contribution rate of each directional attribute volume is calculated.

[0165] The specific steps are as follows:

[0166] ①After processing, a five-dimensional pre-stack set is generated;

[0167] ② A finite azimuth superposition generates an azimuth superposition body;

[0168] ③ Attribute calculation forms a directional attribute body;

[0169] ④ Crack inversion yields anisotropic and oriented bodies;

[0170] ⑤ Ellipse analysis yields the azimuth contribution rate;

[0171] ⑥ The azimuth cumulative quantities are linearly added to obtain the azimuth reduced volume.

[0172] The specific steps are described in detail below:

[0173] Step 1: Preprocess the seismic data to generate a five-dimensional pre-stack gather;

[0174] Preprocessing is performed on "two-width and one-height" seismic data. This preprocessing typically includes, but is not limited to, trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction. In this embodiment, the formation of the five-dimensional pre-stack gather requires omnidirectional migration imaging while preserving azimuth and offset.

[0175] Step 2: Perform finite azimuth stacking on the offset front azimuth offset (azimuth incident angle) seismic gathers to generate an azimuth stack;

[0176] For seismic gathers with offset front-stack offset (azimuth incident angle), multiple azimuth stacks are formed by stacking partial azimuth angles. In the example of this invention, a total of 6 azimuth stacks are formed, namely: first azimuth stack, second azimuth stack, third azimuth stack, fourth azimuth stack, fifth azimuth stack, and sixth azimuth stack.

[0177] The first azimuth superposition is superimposed according to azimuth angles of 1 degree to 30 degrees, with azimuth angle Azi1 denoted as 15 degrees;

[0178] The second azimuth superposition is superimposed according to azimuth angles of 31 degrees to 60 degrees, with azimuth angle Azi2 denoted as 45 degrees;

[0179] The third-order superposition body is superimposed according to the azimuth angle of 61 degrees to 90 degrees, and the azimuth angle Azi3 is recorded as 75 degrees;

[0180] The fourth azimuth superposition is superimposed according to azimuth angles of 91 degrees to 120 degrees, with azimuth angle Azi4 denoted as 115 degrees;

[0181] The fifth azimuth superposition body is superimposed with azimuth angles of 121 degrees to 150 degrees, and the azimuth angle Azi5 is 135 degrees;

[0182] The sixth azimuth superposition is formed by superimposing azimuth angles from 151 degrees to 180 degrees, with azimuth angle Azi6 denoted as 165 degrees.

[0183] Step 3: Perform attribute calculations based on each directional superposition to form a directional attribute body corresponding to each directional superposition.

[0184] Each azimuth stack obtained through finite azimuth stacking has the same data as a conventional post-stack 3D data volume. Therefore, high-precision 3D attribute calculations can be performed based on each azimuth stack, such as calculating one or more of the following attributes: intrinsic coherence, curvature, seismic tensor, edge detection, ants, etc. In the example of this invention, six azimuth attribute volumes are formed based on the above six azimuth stacks.

[0185] Step 4: Crack inversion yields anisotropic and azimuthal volumes, i.e., anisotropic intensity and inversion azimuth angle φ.

[0186] Crack inversion is performed on five-dimensional pre-stack gathers processed by omnidirectional migration imaging to obtain anisotropic intensity and azimuth. Numerous articles and patents exist on obtaining crack parameters based on AVAZ inversion, so they will not be elaborated upon here.

[0187] Step 5: Ellipse analysis yields the azimuth contribution rate;

[0188] Early anisotropic crack parameter inversion was based on elliptic fitting, where crack strength (i.e., anisotropic strength) is the ellipticity, defined as the ratio of minor axis to major axis, and the definition of azimuth φ is consistent with the inversion azimuth φ in step four.

[0189] The technical solution of this embodiment draws on the concept of ellipticity, and the anisotropic intensity obtained in step four is recorded as the value of ellipticity.

[0190] For ease of understanding, in such Figure 2 In the ellipse shown,

[0191] The length of the major axis is a;

[0192] The length of the minor axis is b;

[0193] The ellipticity η of an ellipse is b / a.

[0194] The azimuth angle of the ellipse in coordinate system MON is φ. For a certain azimuth angle Azi (Azi1, Azi2, Azi3, Azi4, Azi5, Azi6), in... Figure 2 In this case, Azi is the included angle of MON. The axis length OP of this azimuth angle Azi is denoted as ρ.

[0195] For a given azimuth property, its azimuth angle Azi is the angle between MOP and MOP, i.e., Azi = θ + φ; the angle between OP and the major axis a of the standard ellipse is θ = Azi - φ.

[0196] Here, point O is any one of the six directional attribute volumes. According to the standard equation of an ellipse, point O in the xOy coordinate system has the following relationship with its x and y coordinates:

[0197]

[0198] Since the axis length of point O is ρ, from Figure 2 As can be seen from this, x = ρcosθ and y = ρsinθ. Let the value of the major axis a be 1, and by substituting a and b into the above standard equation using the value of the anisotropic strength b, we can obtain the value of the axis length ρ.

[0199] In this embodiment, there are a total of six azimuth angles. For a certain CDP (Line, Cdp), the anisotropy intensity obtained from the inversion in step four is 0.2, and the azimuth angle is 15 degrees (here, the azimuth of the major axis is 15 degrees, i.e., = 15). Therefore, the angles θ = Azi - φ between the six azimuth attribute volumes and the major axis can be calculated as 0 degrees, 30 degrees, 60 degrees, 90 degrees, 120 degrees, and 150 degrees, respectively. (Note: For ease of calculation, the angle θ is intentionally set to an integer. Of course, this division of azimuth angles is also quite common in daily use.)

[0200] Taking the azimuth attribute volume (i.e., the third azimuth attribute volume) with an azimuth angle φ of 45 degrees as an example, the anisotropy intensity in step four is 0.2. At this time, a = 1 and b = 0.25. Through the above standard equation, ρ3 = 0.285 can be obtained.

[0201] The specific calculation method is as follows: Substitute a = 1, b = 0.25, θ + φ = 30 degrees into the equation, which is:

[0202]

[0203] Using trigonometric functions, we know sin30 = 0.5 and cos30 = 0.866, and thus ρ3 = 0.285.

[0204] The ρ values ​​of the other directional attribute bodies can be obtained sequentially, denoted as (ρ1, ρ2, ρ3, ρ4, ρ5, ρ6). From the trigonometric functions above, we know that the values ​​of ρ1 to ρ6 are between (0.25, 1).

[0205] According to anisotropy theory, the amplitude difference is greatest when it is perpendicular to the azimuth development direction. Therefore, it is necessary to find the maximum azimuth anisotropy value. However, due to the 90-degree uncertainty inherent in AVAZ inversion (i.e., it is impossible to determine which direction, the major or minor axis, is correct), we can simultaneously calculate the magnitudes of two sets of scale values. First, we take ρmax = MAX(ρ1, ρ2, ρ3, ρ4, ρ5, ρ6) and ρmin = MIN(ρ1, ρ2, ρ3, ρ4, ρ5, ρ6).

[0206] Calculate the anisotropy contribution rates for the two types of six-position bodies respectively: ρ1 / ρmax, ρ2 / ρmax, ρ3 / ρmax, ρ4 / ρmax, ρ5 / ρmax, ρ6 / ρmax; and ρ1 / ρmin, ρ2 / ρmin, ρ3 / ρmin, ρ4 / ρmin, ρ5 / ρmin, ρ6 / ρmin.

[0207] By repeating the above steps along the entire 3D volume's line direction, the orientational anisotropy contribution rate of all CDP points in the entire 3D volume can be obtained.

[0208] Step Six: Dimensionality Reduction of N Directional Overlay Attributes

[0209] Based on the anisotropic contribution rates of the six directions obtained in step five, the result of the fusion of the six directional attribute volumes can be calculated by linearly adding them together, and can be denoted as:

[0210] Mmax=N1*ρ1 / ρmax+N2*ρ2 / ρmax+N3*ρ3 / ρmax+N4*ρ4 / ρmax+N5*ρ5 / ρmax+N6*ρ6 / ρmax;

[0211] or

[0212] Mmin=N1*ρ1 / ρmin+N2*ρ2 / ρmin+N3*ρ3 / ρmin+N4*ρ4 / ρmin+N5*ρ5 / ρmin+N6*ρ6 / ρmin;

[0213] Note: N1 to N6 are the values ​​of the three-dimensional volume attributes.

[0214] Figures 3a to 3f The example only shows slices along the layers of the 3D volume. The actual calculation should use the overall attribute volume in the 3D work area. According to the example in the previous steps, the value range of ρ1 to ρ6 is (0.2,1). Therefore, it is inevitable that one of the coefficients, such as ρ6 / ρmax or ρ1 / ρmin = 1. This is consistent with the development of anisotropy, because all attribute results are relative. What we need is to find the relative maximum or minimum contribution rate in different orientation attributes based on the intensity and orientation of anisotropy, so as to achieve dimensionality reduction (i.e., relative attribute enhancement). Therefore, it is acceptable for the attribute value range after dimensionality reduction to be different from the value range of N1 to N6.

[0215] Based on the understanding of the properties of the superimposed body, the calculated Mmax and Mmin can be compared and analyzed to obtain the correct result. In most cases, since the regional tectonic stress direction is often consistent in a work area, there is only one possible crack development direction, so only one of the Mmax and Mmin results is correct.

[0216] However, it cannot be ruled out that in some work areas, both results may reflect the preferences of certain regions. In such cases, it is necessary to analyze each specific problem in detail.

[0217] Figures 3a to 3f Slicing coherent volumes along layers for six azimuth attributes of a certain seismic work area; Figure 4 This is a coherent slice along the layers of a post-stack seismic profile; Figure 5 Orientation attribute dimensionality reduction volume slices along layers based on elliptic analysis. Figure 5 and Figure 4 The comparison reveals that the azimuth coherence attribute dimensionality reduction technique based on elliptic analysis is significantly superior to the coherence attribute of single post-stack data.

[0218] In summary, this application example demonstrates an azimuth attribute dimensionality reduction technique based on elliptic analysis. For a five-dimensional seismic gather obtained through "two widths and one height" processing, post-stack attributes are calculated to obtain the azimuth attribute volume. AVAZ inversion is then performed on the five-dimensional seismic gather to obtain the anisotropy (fracture intensity) volume and the fracture azimuth volume. Regression analysis is performed on anisotropy and azimuth based on elliptic analysis to obtain the anisotropy contribution rate of different azimuth attribute volumes. The anisotropy contribution rates of different azimuth volumes are linearly added to obtain the dimensionality reduction result of the azimuth attribute. This application example calculates the anisotropy contribution rate for each azimuth attribute volume based on the elliptic analysis regression approach.

[0219] Example 7

[0220] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of the above embodiment.

[0221] The aforementioned storage medium can be flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, app store, etc. The method in the above embodiments includes:

[0222] Attribute calculations are performed on a preset number of directional superimposed objects to obtain the directional attribute object corresponding to each directional superimposed object;

[0223] Inversion is performed on the seismic gathers to obtain the anisotropy intensity and inversion azimuth.

[0224] Elliptical analysis is performed on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume.

[0225] The directional contribution rates of a preset number of locations are linearly summed to obtain the dimensionality reduction result of the directional attributes.

[0226] In some embodiments, before the step of performing attribute calculations on a preset number of azimuth superimposed volumes to obtain the azimuth attribute volume corresponding to each azimuth superimposed volume, the method further includes:

[0227] The seismic gathers are azimuthally superimposed to generate a preset number of azimuth superimposed volumes.

[0228] In some embodiments, prior to the step of azimuth stacking of seismic gathers, the method further includes:

[0229] Seismic data is preprocessed to generate seismic gathers;

[0230] The preprocessing includes one or more of the following: trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction.

[0231] Seismic data is obtained through omnidirectional migration imaging, and the seismic data includes azimuth angles.

[0232] In some embodiments, in the step of calculating the attributes of a preset number of azimuth superimposed volumes, the attributes include one or more of the following: intrinsic coherence attributes, curvature attributes, seismic tensor attributes, edge detection attributes, and ant attributes.

[0233] In some embodiments, in the step of performing elliptic analysis on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume, the elliptic analysis includes:

[0234] Using the anisotropy intensity as the ellipticity, and combining the inverted azimuth angle with the azimuth angle of each azimuth attribute volume, elliptic analysis is performed on the corresponding azimuth attribute volume to obtain the corresponding axis length of each azimuth attribute volume.

[0235] Based on the corresponding axis length of each directional attribute body, calculate the directional contribution rate of each directional attribute body.

[0236] In some embodiments, the step of calculating the azimuth contribution rate of each azimuth attribute volume based on the corresponding axis length of each azimuth attribute volume includes:

[0237] Obtain the maximum and minimum axis lengths from the set of axis lengths corresponding to each orientation attribute body;

[0238] The ratio of the corresponding axis length of each directional attribute body to the maximum or minimum axis length is taken as the directional contribution rate of the corresponding directional attribute body.

[0239] In some embodiments, the step of linearly summing a preset number of azimuth contribution rates to obtain a dimensionality reduction result of the azimuth attribute includes:

[0240] The attribute value of each directional attribute is multiplied by the directional contribution rate and then summed to obtain the dimensionality reduction result of the directional attribute.

[0241] The technical solution of this embodiment calculates the attributes of a preset number of azimuth stacks to obtain the azimuth attribute volume corresponding to each azimuth stack; it inverts the seismic gathers to obtain the anisotropy intensity and inversion azimuth; it performs elliptic analysis on each azimuth attribute volume based on the anisotropy intensity and inversion azimuth to obtain the azimuth contribution rate of each azimuth attribute volume; and it linearly adds the preset number of azimuth contribution rates to obtain the dimensionality reduction result of the azimuth attributes. This enables the dimensionality reduction optimization and fusion of multiple azimuth attribute volumes, thereby enabling efficient utilization of "two-wide and one-high" seismic data, fully mining the azimuth information in pre-stack seismic data, and thus achieving effective characterization of complex geological anomalies such as faults-fissures and river channels.

[0242] Other technical features and effects of this embodiment are the same as those of the other embodiments described above, and will not be repeated here.

[0243] Example 8

[0244] This embodiment provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the method of the above embodiment.

[0245] The processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the methods in the above embodiments.

[0246] The memory can be implemented from 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 storage, flash memory, magnetic disk, or optical disk. The method in the above embodiments includes:

[0247] Attribute calculations are performed on a preset number of directional superimposed objects to obtain the directional attribute object corresponding to each directional superimposed object;

[0248] Inversion is performed on the seismic gathers to obtain the anisotropy intensity and inversion azimuth.

[0249] Elliptical analysis is performed on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume.

[0250] The directional contribution rates of a preset number of locations are linearly summed to obtain the dimensionality reduction result of the directional attributes.

[0251] In some embodiments, before the step of performing attribute calculations on a preset number of azimuth superimposed volumes to obtain the azimuth attribute volume corresponding to each azimuth superimposed volume, the method further includes:

[0252] The seismic gathers are azimuthally superimposed to generate a preset number of azimuth superimposed volumes.

[0253] In some embodiments, prior to the step of azimuth stacking of seismic gathers, the method further includes:

[0254] Seismic data is preprocessed to generate seismic gathers;

[0255] The preprocessing includes one or more of the following: trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction.

[0256] Seismic data is obtained through omnidirectional migration imaging, and the seismic data includes azimuth angles.

[0257] In some embodiments, in the step of calculating the attributes of a preset number of azimuth superimposed volumes, the attributes include one or more of the following: intrinsic coherence attributes, curvature attributes, seismic tensor attributes, edge detection attributes, and ant attributes.

[0258] In some embodiments, in the step of performing elliptic analysis on each azimuth attribute volume based on anisotropy intensity and inversion azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume, the elliptic analysis includes:

[0259] Using the anisotropy intensity as the ellipticity, and combining the inverted azimuth angle with the azimuth angle of each azimuth attribute volume, elliptic analysis is performed on the corresponding azimuth attribute volume to obtain the corresponding axis length of each azimuth attribute volume.

[0260] Based on the corresponding axis length of each directional attribute body, calculate the directional contribution rate of each directional attribute body.

[0261] In some embodiments, the step of calculating the azimuth contribution rate of each azimuth attribute volume based on the corresponding axis length of each azimuth attribute volume includes:

[0262] Obtain the maximum and minimum axis lengths from the set of axis lengths corresponding to each orientation attribute body;

[0263] The ratio of the corresponding axis length of each directional attribute body to the maximum or minimum axis length is taken as the directional contribution rate of the corresponding directional attribute body.

[0264] In some embodiments, the step of linearly summing a preset number of azimuth contribution rates to obtain a dimensionality reduction result of the azimuth attribute includes:

[0265] The attribute value of each directional attribute is multiplied by the directional contribution rate and then summed to obtain the dimensionality reduction result of the directional attribute.

[0266] The technical solution of this embodiment calculates the attributes of a preset number of azimuth stacks to obtain the azimuth attribute volume corresponding to each azimuth stack; it inverts the seismic gathers to obtain the anisotropy intensity and inversion azimuth; it performs elliptic analysis on each azimuth attribute volume based on the anisotropy intensity and inversion azimuth to obtain the azimuth contribution rate of each azimuth attribute volume; and it linearly adds the preset number of azimuth contribution rates to obtain the dimensionality reduction result of the azimuth attributes. This enables the dimensionality reduction optimization and fusion of multiple azimuth attribute volumes, thereby enabling efficient utilization of "two-wide and one-high" seismic data, fully mining the azimuth information in pre-stack seismic data, and thus achieving effective characterization of complex geological anomalies such as faults-fissures and river channels.

[0267] Other technical features and effects of this embodiment are the same as those of the other embodiments described above, and will not be repeated here.

[0268] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0269] It should be noted that, in this invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0270] While the embodiments disclosed in this invention are as described above, the above content is merely for the purpose of facilitating understanding of this invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this invention; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for dimensionality reduction of orientation attributes, characterized in that, include: Attribute calculations are performed on a preset number of directional superimposed objects to obtain the directional attribute object corresponding to each directional superimposed object; Inversion is performed on the seismic gathers to obtain the anisotropy intensity and inversion azimuth. Elliptical analysis is performed on each azimuth attribute volume based on the anisotropy intensity and the inverted azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume. The directional contribution rates of a preset number of locations are linearly summed to obtain the dimensionality reduction result of the directional attributes; In the step of performing elliptic analysis on each azimuth attribute volume based on the anisotropy intensity and the inverted azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume, the elliptic analysis includes: Using the anisotropy intensity as the ellipticity, and combining the inverted azimuth angle with the azimuth angle of each azimuth attribute volume, elliptic analysis is performed on the corresponding azimuth attribute volume to obtain the corresponding axis length of each azimuth attribute volume. Obtain the maximum and minimum axis lengths from the set of axis lengths corresponding to each orientation attribute body; The ratio of the corresponding axis length of each directional attribute body to the maximum axis length or the minimum axis length is taken as the directional contribution rate of the corresponding directional attribute body.

2. The orientation attribute dimensionality reduction method according to claim 1, characterized in that, Before the step of performing attribute calculations on a preset number of azimuth overlays to obtain the azimuth attribute body corresponding to each azimuth overlay, the method further includes: The seismic gathers are azimuthally superimposed to generate a preset number of azimuth superimposed volumes.

3. The orientation attribute dimensionality reduction method according to claim 2, characterized in that, Prior to the step of azimuth stacking of the seismic gathers, the method further includes: Seismic data is preprocessed to generate seismic gathers; The preprocessing includes one or more of the following: trace editing, bandpass filtering, true amplitude recovery, static correction, velocity analysis, residual static correction, surface amplitude consistency compensation, pre-stack deconvolution, and dynamic correction. The seismic data is obtained through omnidirectional migration imaging, and the seismic data includes azimuth angles.

4. The orientation attribute dimensionality reduction method according to claim 1, characterized in that, In the step of calculating the attributes of a preset number of azimuth superimposed volumes, the attributes include one or more of the following: intrinsic coherence attributes, curvature attributes, seismic tensor attributes, edge detection attributes, and ant attributes.

5. The orientation attribute dimensionality reduction method according to claim 1, characterized in that, The step of linearly adding the directional contribution rates of a preset number to obtain the dimensionality reduction result of the directional attribute includes: The attribute value of each directional attribute is multiplied by the directional contribution rate and then summed to obtain the dimensionality reduction result of the directional attribute.

6. A directional attribute dimensionality reduction device based on any one of the directional attribute dimensionality reduction methods of claims 1 to 5, characterized in that, include: The attribute calculation module is used to perform attribute calculations on a preset number of directional superimposed bodies to obtain the directional attribute body corresponding to each directional superimposed body. The inversion module is used to invert seismic gathers to obtain anisotropy intensity and inversion azimuth. The ellipse analysis module is used to perform ellipse analysis on each azimuth attribute volume based on the anisotropy intensity and the inverted azimuth angle to obtain the azimuth contribution rate of each azimuth attribute volume. The dimensionality reduction module is used to linearly add up a preset number of directional contribution rates to obtain the dimensionality reduction result of the directional attributes.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

8. An electronic device comprising a processor and a memory, characterized in that, The memory stores a computer program, and the processor executes the computer program to implement the method of any one of claims 1 to 5.

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