High-angle fracture identification method based on diffusion equation

Abstract

The invention discloses a high-angle fracture identification method based on a diffusion equation, and aims to solve the problem that the fault trend is inconsistent with original seismic data in an existing anisotropic diffusion method. According to the method, on the basis of a traditional diffusion model based on a structure tensor, a direction consistency enhancement mechanism is introduced, fault main direction information is extracted through a rho-theta spectrum, and a continuous diffusion tensor in the fault direction is constructed. The diffusivity is guided and filtered, so that the diffusivity has higher direction consistency at the fault position, and the continuity and accuracy of fault detection are improved. After processing, noise can be effectively suppressed, the difference between the fault and the seismic event can be enhanced, the extracted fault result is more suitable for the actual geological structure, and the method is particularly suitable for seismic data processing and fracture system modeling in the complex geological environment.

Description

Technical Field

[0001] The present invention belongs to the fields of seismic exploration and geological engineering, and in particular relates to a high-angle fracture identification method based on a diffusion equation. Background Art

[0002] In the fields of seismic exploration and geological engineering, accurately identifying underground faults and their characteristics is an important part of understanding geological structures, assessing earthquake risks, and conducting resource exploration. Underground geological conditions are often complex, resulting in instability and ambiguity in seismic signals. Therefore, the importance of efficiently processing seismic data to suppress noise and enhance key features (such as faults and event axes) has become increasingly prominent. To this end, researchers have proposed a variety of denoising and feature extraction methods, among which anisotropic diffusion filtering technology based on the diffusion equation has become an important research direction. This technology aims to highlight geological features of interest, such as faults and the propagation direction of seismic waves, by defining the diffusion tensor and its diffusion rate in different directions.

[0003] Existing fault identification methods based on diffusion equations have some significant shortcomings in the identification of high-angle faults. First, the smoothing direction of the diffusion rate is usually perpendicular to the phase axis of the seismic data, which causes the fault strike extracted from the data to deviate from the strike in the original data, affecting the identification accuracy. Second, in complex geological environments, the noise in the seismic signal often seriously interferes with the diffusion process, resulting in unstable analysis results and misjudgment. Finally, when dealing with high-angle faults, traditional methods lack the need to strengthen directional consistency, and the identified faults are often not continuous and clear enough, affecting the effectiveness of subsequent geological analysis and modeling. Summary of the Invention

[0004] In order to solve the technical problems existing in the background technology, the present invention aims to provide a high-angle fracture identification method based on the diffusion equation to improve the accuracy and consistency of fault identification and provide more reliable technical support for the analysis of complex underground structures.

[0005] In order to solve the technical problem, the technical solution of the present invention is:

[0006] A high-angle fault identification method based on the diffusion equation is proposed. The method first preprocesses the seismic data, calculates the structural tensor and performs eigendecomposition, and constructs the diffusion tensor and diffusivity. The method then extracts the main direction of the fault by combining the ρ-θ energy spectrum, constructs diffusion vectors with consistent directions and an enhanced diffusion tensor, performs consistency-enhanced diffusion processing, extracts fault ridges, and determines spatial trend information, thus achieving high-precision extraction and analysis of fault characteristics.

[0007] Furthermore, the method comprises the following steps:

[0008] S1: Collect and import raw seismic data, including seismic signals, seismic gradients, and related metadata, and preprocess the raw seismic data, including denoising, normalization, and data enhancement, to obtain high-quality preprocessed seismic data;

[0009] S2: Based on the preprocessed seismic data, the gradient of the seismic data is calculated and the structural tensor T is constructed. The formula is: T = λ u uu T +λ v vv T +λ w ww T , perform eigendecomposition and obtain the eigenvalue λ u ,λ v ,λ w and the corresponding eigenvectors u, v, w;

[0010] Based on the structure tensor T, a diffusion tensor and a diffusion rate are defined;

[0011] Diffusion tensor D(x), D(x) = μ v (x)v(x)v T (x)+μ w (x)w(x)w T (x), calculate the diffusion rate s(x), and define the diffusion rate according to the directional derivative |d| and the threshold α:

[0012]

[0013] Obtain a data structure containing a structure tensor, a diffusion tensor D(x), and a diffusion rate s(x);

[0014] S3: Based on the structural tensor, diffusion tensor and diffusion rate output in step S2, the diffusion equation is used to perform preliminary guided diffusion on the seismic data, and the main direction θ of the potential fault is identified through ρ-θ energy spectrum analysis, and the consistency enhancement vector is constructed. Constructing the enhanced diffusion tensor The guiding term H is introduced to adjust the diffusion rate:

[0015]

[0016] Diffusion processing after consistency enhancement is performed to obtain optimized seismic data, and seismic data with enhanced consistency and new diffusivity distribution are output, which preserves the fault direction information.

[0017] Furthermore, after step S3, the method further includes:

[0018] S4: Using the enhanced seismic data output from step S3 (retaining fault features), extract ridges / edges as candidate fault locations, use geometric methods (such as principal curvature, Hough transform) to determine the fault boundary and strike, generate a spatial distribution map and attribute information (strike, dip) of the fault, compare and evaluate the extracted results with the original data to verify the accuracy. If the recognition accuracy is insufficient, return to S2 or S3 to adjust the parameters and output the final fault recognition result, including the fault location, strike, and strength information, for use in geological modeling.

[0019] Compared with the prior art, the advantages of the present invention are:

[0020] This method introduces a consistency enhancement mechanism to optimize the continuity and accuracy of the diffusion rate along the fault direction, making the extracted fault strikes closer to the true structure in the original seismic data. This addresses the strike bias issue inherent in traditional diffusion methods. This significantly improves the accuracy of high-angle fault identification and the consistency of seismic data interpretation, making it particularly suitable for processing seismic data in complex geological conditions and with significant noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 , a diagram of calculation results proposed by the prior art in this embodiment;

[0022] Figure 2 , a calculation result diagram of a high-angle fracture identification method based on the diffusion equation of the present invention. DETAILED DESCRIPTION

[0023] The specific implementation of the present invention is described below in conjunction with examples:

[0024] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0025] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0026] Example 1:

[0027] In order to better highlight the differences between faults and non-faults, existing technologies use a fracture system processing method based on the diffusion equation:

[0028]

[0029] D(x)=μ v (x)v(x)v T (x)+μ w (x)w(x)w T (x)

[0030] Where D(x) is the diffusion tensor of the analysis point, which is constructed by the eigenvectors of the seismic data structure tensor. The structure tensor T of seismic data can be constructed as the smoothed outer product of the seismic data gradient. The structure tensor T can be expressed by eigendecomposition as follows:

[0031] T=λ u uu T +λ v vv T +λ w ww T

[0032] Among them, λ u ,λ v ,λ w are the eigenvalues ​​corresponding to the orthogonal eigenvectors u, v, and w respectively. If the mark λ u ≥λ v ≥λ w ≥0, the corresponding eigenvector u will be parallel to the direction where the u image changes most significantly, and the corresponding eigenvector w will be parallel to the direction where the u image changes least significantly, based on which the original seismic data is diffused.

[0033] The defined diffusion rate in this technology diffusion process is:

[0034]

[0035] Where α is the threshold for detecting discontinuities at the analysis point, and |d| is the directional derivative of the analysis point. The relationship between the directional derivative and the threshold is used to distinguish between faults and noise, controlling the diffusion direction of the analysis point. When |d| > α, the point is considered a discontinuity and retained; otherwise, it is considered noise and suppressed.

[0036] Then the diffusion rate is further refined and the ridge is used to express the fault direction information. The calculation results are as follows: Figure 1 A disadvantage of the first prior art is that the orientation of the enhanced faults is poorly consistent with the fault strike in the original data. This is because the direction of the final diffusivity smoothing in this method is always perpendicular to the event axis of the seismic data, so the final extracted fracture strike deviates from the fault strike in the original data.

[0037] The present invention proposes a high-angle diffusion processing method based on the diffusion equation, which can better solve the problem of mismatch between the ridge direction and the fault direction in the original data and meet the needs of fracture system modeling.

[0038] In order to make the identified fault information more consistent with the original seismic data, the present invention introduces a consistency enhancement processing method based on the existing technology.

[0039] Highlighting the differences between faulted and non-faulted conditions, a fracture system treatment based on the diffusion equation is used:

[0040]

[0041] D(x)=μ v (x)v(x)v T (x)+μ w (x)w(x)w T (x)

[0042] Where D(x) is the diffusion tensor of the analysis point, which is constructed by the eigenvectors of the seismic data structure tensor. The structure tensor T of seismic data can be constructed as the smoothed outer product of the seismic data gradient. The structure tensor T can be expressed by eigendecomposition as follows:

[0043] T=λ u uu T +λ v vv T +λ w ww T

[0044] Among them, λ u ,λ v ,λ w are the eigenvalues ​​corresponding to the orthogonal eigenvectors u, v, and w respectively. If the mark λ u ≥λ v ≥λ w ≥0, the corresponding eigenvector u will be parallel to the direction where the u image changes most significantly, and the corresponding eigenvector w will be parallel to the direction where the u image changes least significantly, based on which the original seismic data is diffused.

[0045] The defined diffusion rate in this technology diffusion process is:

[0046]

[0047] Where α is the threshold for detecting discontinuities at the analysis point, and |d| is the directional derivative of the analysis point. The relationship between the directional derivative and the threshold is used to distinguish between faults and noise, controlling the diffusion direction of the analysis point. When |d| > α, the point is considered a discontinuity and retained; otherwise, it is considered noise and suppressed.

[0048] The basic principle is to perform voting in parameter space to determine the shape of an object, determined by the local maximum in the ρ-θ spectrum. The basic process is: ① Calculate the ρ-θ energy spectrum of the image data; ② Determine the parameters ρ and θ of the line based on the local maximum in the ρ-θ energy spectrum; ③ Finally, determine the line based on the ρ and θ parameters.

[0049] ρ=x cosθ+y sinθ

[0050] The direction of the vector along the straight line can be obtained based on the geometric relationship, and the direction of the point on the non-straight line can be obtained by interpolation.

[0051]

[0052] Then the diffusion tensor that diffuses only along the direction of this vector is

[0053] From the perspective of seismic data filtering, filtering should be stopped at the fault position, that is, the diffusivity should be zero. However, the actual diffusivity obtained at the fault position does not show a zero value line along the fault, but corresponds to many low-value points. Therefore, it is necessary to filter these low-value points along the fault direction. The above consistency enhancement process can detect points on the same straight line and determine their linear direction. According to the linear direction, the diffusivity is then subjected to consistency enhancement, so that the diffusivity can be made more continuous along the fault and the direction is more consistent with the actual fault direction. The diffusivity of the diffusion tensor constructed at this time is:

[0054]

[0055] Among them, μ u 、μ v 、μ w represents the diffusion rate in the u, v, and w directions. H represents the filter used to guide the diffusion rate during consistency enhancement. α is the threshold used to detect discontinuities at the analysis point. |d| is the directional derivative of the analysis point. The relationship between the directional derivative and the threshold is used to distinguish between faults and noise and control the diffusion direction of the analysis point. When |d| > α, the point is considered a discontinuity and retained; otherwise, it is considered noise and suppressed.

[0056] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0058] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0060] On this basis, further refinement and ridge-extraction processing of the diffusivity attributes can obtain fault identification results that are consistent with the original seismic data.

[0061] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

[0062] Many other changes and modifications can be made without departing from the spirit and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A high-angle fracture identification method based on diffusion equation, characterized in that: The method first preprocesses the seismic data, calculates the structural tensor and performs eigendecomposition, and constructs the diffusion tensor and diffusivity. It then extracts the main direction of the fault by combining the ρ-θ energy spectrum, constructs diffusion vectors with consistent directions and an enhanced diffusion tensor, performs consistency-enhanced diffusion processing, extracts fault ridges, and determines spatial trend information, thus achieving high-precision extraction and analysis of fault characteristics.

2. The high-angle fracture identification method based on the diffusion equation according to claim 1 is characterized in that: The method comprises the following steps: S1: Collect and import raw seismic data, including seismic signals, seismic gradients, and related metadata, and preprocess the raw seismic data, including denoising, normalization, and data enhancement, to obtain high-quality preprocessed seismic data; S2: Based on the preprocessed seismic data, the gradient of the seismic data is calculated and the structural tensor T is constructed. The formula is: T = λ u uu T +λ v vv T +λ w ww T , perform eigendecomposition and obtain the eigenvalue λ u ,λ v ,λ w and the corresponding eigenvectors u, v, w; Based on the structure tensor T, a diffusion tensor and a diffusion rate are defined; Diffusion tensor D(x), D(x) = μ v (x)v(x)v T (x)+μ w (x)w(x)w T (x), calculate the diffusion rate s(x), and define the diffusion rate according to the directional derivative |d| and the threshold α: Obtain a data structure containing a structure tensor, a diffusion tensor D(x), and a diffusion rate s(x); S3: Based on the structural tensor, diffusion tensor and diffusion rate output in step S2, the diffusion equation is used to perform preliminary guided diffusion on the seismic data, and the main direction θ of the potential fault is identified through ρ-θ energy spectrum analysis, and the consistency enhancement vector is constructed. Constructing the enhanced diffusion tensor The guiding term H is introduced to adjust the diffusion rate: Diffusion processing after consistency enhancement is performed to obtain optimized seismic data, and seismic data with enhanced consistency and new diffusivity distribution are output, which preserves the fault direction information.

3. The high-angle fracture identification method based on the diffusion equation according to claim 1 is characterized in that: After step S3, the method further includes: S4: Using the enhanced seismic data output from step S3 (retaining fault features), extract ridges / edges as candidate fault locations, use geometric methods (such as principal curvature, Hough transform) to determine the fault boundary and strike, generate a spatial distribution map and attribute information (strike, dip) of the fault, compare and evaluate the extracted results with the original data to verify the accuracy. If the recognition accuracy is insufficient, return to S2 or S3 to adjust the parameters and output the final fault recognition result, including the fault location, strike, and strength information, for use in geological modeling.

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