Geological structure edge detection method based on enhanced gravity tensor eigenvalue

Abstract

The invention is suitable for the field of geophysics, and provides a geologic structure edge detection method based on an enhanced gravity tensor eigenvalue, and the method comprises the steps: preparing data, and generating a gravity gradient tensor matrix; performing characteristic decomposition on the gravity gradient tensor matrix to obtain a gravity tensor characteristic value; performing deep decomposition on the gravity tensor characteristic value to extract amplitude information; constructing an enhanced edge detection filter F based on the amplitude information; according to the above steps, the structure edge detection calculation based on the enhanced gravity tensor characteristic value is realized, and the underground geologic structure edge is obtained. Compared with a common characteristic value detection technology, the geological structure (or target body) edge detection result obtained through the method technology can obtain a clearer and more continuous recognition effect, the positioning accuracy is high, the method can be well suitable for edge description of the geological structure (or target body) with multiple scales and different buried depth superposition, and the practicability is higher.

Description

Technical Field

[0001] The present invention relates to the field of geophysics, and in particular to a geological structure edge detection method based on enhanced gravity tensor eigenvalues. Background Art

[0002] In geophysical exploration, gravity measurements are an important means of detecting the spatial location and distribution of subsurface materials or objects. The emerging gravity tensor gradient measurement technology has significantly improved the signal-to-noise ratio and reliability of gravity detection and interpretation. The gravity tensor eigenvalues, with their high horizontal resolution, provide a powerful tool for interpreting geological body morphology and identifying structural edges or boundary features. However, existing methods for detecting geological structure edges based on gravity tensor eigenvalues ​​primarily rely on the largest eigenvalue of the gravity tensor matrix itself. In practical applications, such methods are often affected by geological structure morphology and geometric parameters, resulting in limited edge location accuracy. Furthermore, they are sensitive to variations in geological body depth, making it difficult to precisely characterize the edge distribution characteristics of geological bodies at varying depths. To overcome these limitations, the present invention proposes a method for detecting geological structure edges based on enhanced gravity tensor eigenvalues. This method can significantly improve edge detection accuracy and resolution in complex geological bodies (especially those with varying depths or overlapping geological bodies), thereby effectively enhancing the accuracy of gravity data interpretation. Summary of the Invention

[0003] In view of the deficiencies of the prior art, the purpose of the present invention is to propose a geological structure edge detection method based on enhanced gravity tensor eigenvalues ​​to solve the technical problems mentioned in the background technology.

[0004] A geological structure edge detection method based on enhanced gravity tensor eigenvalues, the method comprising:

[0005] S1, data preparation, generating gravity gradient tensor matrix;

[0006] S2. Decompose the rows of the gravity gradient tensor matrix to obtain the eigenvalues ​​of the gravity tensor;

[0007] S3. Deeply decompose the gravity tensor eigenvalue to extract amplitude information;

[0008] S4, constructing an enhanced edge detection filter F based on the amplitude information;

[0009] S5. According to steps S1-S4, structural edge detection calculation based on enhanced gravity tensor eigenvalues ​​is implemented to obtain clear, continuous, and reliably located edges of underground geological structures (or target bodies).

[0010] Preferably, in step S1, data preparation and generation of the gravity gradient tensor matrix include:

[0011] Obtain any five independent components of the measured gravity tensor gradient in the Cartesian coordinate system to form the gravity gradient tensor matrix T, which is as follows:

[0012] Formula 1: ;

[0013] Where, T xx 、T xy 、T xz 、T yy 、T yz are the five independent components of the gravity gradient tensor, based on the passivity of the gravity field (T xx 2 +T yy 2 +T zz 2 = 0) and the irrotational symmetry (T xy = T yx , T xz = T zx , T yz = T zy ) It can be seen that when there are any five independent components of the gravity gradient tensor, it can be expanded into a complete gravity gradient tensor matrix.

[0014] Preferably, in step S1, if the number of measured components in actual measurement is insufficient, it is necessary to obtain five independent components through gradient component conversion of the gravity field.

[0015] Preferably, in step S2, the content of performing eigendecomposition on the rows of the gravity gradient tensor matrix to obtain the eigenvalues ​​of the gravity tensor includes:

[0016] Perform eigendecomposition on the gravity gradient tensor matrix T of each grid point to obtain three gravity tensor eigenvalues. The eigendecomposition is as shown in Formula 2:

[0017] Formula 2: ;

[0018] Where λ i is the eigenvalue of the gravity tensor, λ1>λ2>λ3, v i is the eigenvector corresponding to each gravity tensor eigenvalue, i=1, 2, 3. In conventional methods, the maximum eigenvalue corresponds to the edge distribution of geological structure, so it is a common choice for edge detection methods.

[0019] Preferably, in step S3, the content of extracting amplitude information from the deep decomposition of the gravity tensor eigenvalues ​​includes:

[0020] The characteristic equation is constructed from the eigenvalue of the gravity tensor, as shown in Formula 3:

[0021] Formula 3: ;

[0022] Where λ is the characteristic solution of the characteristic equation of the gravity tensor, I1 and I2 are the two geometric invariants of the gravity gradient tensor (also called tensor invariants);

[0023] Let A = -I1 / 3 and B = I2 / 2, and we can get the real root expression of the characteristic equation, as shown in Formula 4:

[0024] Formula 4: ;

[0025] By performing a deep decomposition of the gravity tensor eigenvalues, it can be found that each gravity tensor eigenvalue is composed of the same amplitude and different phase angles B, where the amplitude information of the gravity tensor eigenvalue is the key to indicating the edge characteristics of the geological structure (or target body).

[0026] Preferably, in step S4, the contents of constructing the enhanced edge detection filter F based on the amplitude information include:

[0027] Based on the amplitude of the gravity tensor eigenvalue, the three-directional derivatives are further calculated, and the enhanced edge detection filter (Formula 5) is formed by the phase calculation method based on the inverse trigonometric function form. The adjustable parameter p is set (experienced range 1-10) to suppress false edge responses.

[0028] Formula 5: ;

[0029] Where A = -I1 / 3, C is the improved gravity tensor eigenvalue, and F is the enhanced edge detection filter obtained based on the ratio and phase form, which can be calculated by Formula 3.

[0030] Beneficial effects achieved by the present invention:

[0031] This invention achieves a high-precision, high-resolution method for detecting geological structure edges by deeply decomposing the eigenvalues ​​of the gravity tensor and designing an enhanced filter. This method improves the adaptability and reliability of this method for interpreting complex geological structures. Compared to conventional eigenvalue detection techniques, the edge detection results of geological structures (or targets) obtained by this method achieve clearer and more continuous recognition, higher positioning accuracy, and are well-suited for depicting the edges of geological structures (or targets) at multiple scales and varying depths, making them more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The flowchart of a geological structure edge detection method based on enhanced gravity tensor eigenvalues.

[0033] Figure 2This is the superimposed gravity gradient full tensor data diagram of the multi-geobody combination model.

[0034] Figure 3 This is the geological structure edge detection result diagram of the multi-geological body combination model.

[0035] Figure 4 This is the full tensor data diagram of the measured gravity gradient.

[0036] Figure 5 This is the geological structure edge detection result diagram of the measured gravity tensor eigenvalues. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] See also Figure 1 The embodiment of the present invention provides a geological structure edge detection method based on enhanced gravity tensor eigenvalues, the method comprising:

[0039] S1, data preparation, generating gravity gradient tensor matrix;

[0040] Obtain any five independent components of the measured gravity tensor gradient in the Cartesian coordinate system to form the gravity gradient tensor matrix T, which is as follows:

[0041] Formula 1: ;

[0042] Where, T xx 、T xy 、T xz 、T yy 、T yz are the five independent components of the gravity gradient tensor (if the number of measured components is insufficient, it is necessary to convert the gradient components of the gravity field to obtain the five independent components). Based on the passivity of the gravity field (T xx 2 +T yy 2 +T zz 2 = 0) and the irrotational symmetry (T xy = T yx , T xz = T zx , T yz = T zy) It can be seen that when there are any five independent components of the gravity gradient tensor, it can be expanded into a complete gravity gradient tensor matrix.

[0043] S2. Decompose the rows of the gravity gradient tensor matrix to obtain the eigenvalues ​​of the gravity tensor;

[0044] Perform eigendecomposition on the gravity gradient tensor matrix T of each grid point to obtain three gravity tensor eigenvalues. The eigendecomposition is as shown in Formula 2:

[0045] Formula 2: ;

[0046] Where λ i is the eigenvalue of the gravity tensor, λ1>λ2>λ3, v i is the eigenvector corresponding to each gravity tensor eigenvalue, i=1, 2, 3. In conventional methods, the maximum eigenvalue corresponds to the edge distribution of geological structure, so it is a common choice for edge detection methods.

[0047] S3. Deeply decompose the gravity tensor eigenvalue to extract amplitude information;

[0048] The characteristic equation is constructed from the eigenvalue of the gravity tensor, as shown in Formula 3:

[0049] Formula 3: ;

[0050] Where I1 and I2 are the two geometric invariants of the gravity gradient tensor;

[0051] Let A = -I1 / 3 and B = I2 / 2, and we can get the real root expression of the characteristic equation, as shown in Formula 4:

[0052] Formula 4: ;

[0053] By performing a deep decomposition of the gravity tensor eigenvalues, it can be found that each gravity tensor eigenvalue is composed of the same amplitude and different phase angles B, where the amplitude information of the gravity tensor eigenvalue is the key to indicating the edge characteristics of the geological structure (or target body).

[0054] S4, constructing an enhanced edge detection filter F based on the amplitude information;

[0055] Based on the amplitude of the gravity tensor eigenvalue, the three-directional derivatives are further calculated, and the enhanced edge detection filter (Formula 5) is formed by the phase calculation method based on the inverse trigonometric function form. The adjustable parameter p is set (experienced range 1-10) to suppress false edge responses.

[0056] Formula 5: ;

[0057] Where A = -I1 / 3, C is the improved gravity tensor eigenvalue, and F is the enhanced edge detection filter obtained based on the ratio and phase form, which can be calculated by Formula 3.

[0058] S5. According to steps S1-S4, structural edge detection calculation based on enhanced gravity tensor eigenvalues ​​is implemented to obtain clear, continuous, and reliably located edges of underground geological structures (or target bodies).

[0059] A complex model was simulated to verify the reliability and practicality of the method of the present invention. The model consists of four rectangular prisms of different sizes and burial depths. The main consideration is the actual situation of simultaneous distribution of deep and shallow parts and superposition of geological structures with close distances. The observed abnormal data of each component of the gravity gradient tensor generated by the model are as follows: Figure 2 As shown in the figure, the black frame lines are the horizontal position projections corresponding to the edges of each model. By using the gravity gradient tensor component data generated by the model, the conventional geological structure edge method based on the maximum gravity tensor eigenvalue and the method of the present invention are processed respectively, and the following are obtained: Figure 3 The detection results clearly show that conventional edge detection methods based on the maximum eigenvalue λ1 of the gravity tensor can only detect geological boundary features in shallowly buried and large-scale models. However, the enhanced gravity tensor eigenvalue F proposed in this invention can simultaneously indicate the horizontal distribution of the edges of various geological targets. Furthermore, the structural edge detection results of the method of this invention have high image resolution, and the identified positions accurately correspond to the actual positions, indicating that the resolution and accuracy of this method are higher than those of conventional methods.

[0060] In order to further illustrate the applicability and stability of the method, the measured gravity gradient full tensor data in the St. George Bay area of ​​Canada ( Figure 4 ) to obtain the distribution of geological structure boundaries at different scales in the underground space of the region. Figure 5 Figure 1 shows different methods for detecting geological structure edges. The left figure shows the conventional edge detection result based on the maximum eigenvalue of the gravity tensor, while the right figure shows the edge detection result of the method of the present invention. The solid black line in the figure represents the boundary of the regional proven structure distribution, and the dotted line represents the newly discovered hidden geological structure distribution edge. The enhanced gravity tensor eigenvalue edge detection method proposed in this invention has higher resolution, is more consistent with the location of proven structures, and can help explore and reveal the distribution boundaries of small-scale or deeply buried hidden geological structures in the region.

[0061] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the description of the present invention, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A geological structure edge detection method based on enhanced gravity tensor eigenvalues, characterized in that: The method comprises: S1, data preparation, generating gravity gradient tensor matrix; S2. Decompose the rows of the gravity gradient tensor matrix to obtain the eigenvalues ​​of the gravity tensor; S3. Deeply decompose the gravity tensor eigenvalue to extract amplitude information; S4, constructing an enhanced edge detection filter F based on the amplitude information; S5. According to steps S1-S4, structural edge detection calculation based on enhanced gravity tensor eigenvalues ​​is implemented to obtain the underground geological structure edge.

2. The method for detecting geological structure edges based on enhanced gravity tensor eigenvalues ​​according to claim 1, characterized in that: In step S1, data preparation and generation of the gravity gradient tensor matrix include: Obtain any five independent components of the measured gravity tensor gradient in the Cartesian coordinate system to form the gravity gradient tensor matrix T, which is as follows: Formula 1: ; Where, T xx 、T xy 、T xz 、T yy 、T yz are the five independent components of the gravity gradient tensor. Based on the passivity and irrotational symmetry of the gravity field, it can be seen that when there are any five independent components of the gravity gradient tensor, it can be expanded into a complete gravity gradient tensor matrix.

3. The method for detecting geological structure edges based on enhanced gravity tensor eigenvalues ​​according to claim 2, characterized in that: In step S1, if the number of measured components is insufficient in the actual measurement, it is necessary to obtain five independent components through the gradient component conversion of the gravity field.

4. The method for detecting geological structure edges based on enhanced gravity tensor eigenvalues ​​according to claim 1, wherein: In step S2, the gravity gradient tensor matrix is ​​subjected to row eigendecomposition to obtain the gravity tensor eigenvalues, which include: Perform eigendecomposition on the gravity gradient tensor matrix T of each grid point to obtain three gravity tensor eigenvalues. The eigendecomposition is as shown in Formula 2: Formula 2: ; Where λ i is the eigenvalue of the gravity tensor, λ1>λ2>λ3, v i is the eigenvector corresponding to each eigenvalue of the gravity tensor, i=1, 2, 3.

5. The method for detecting geological structure edges based on enhanced gravity tensor eigenvalues ​​according to claim 1, characterized in that: In step S3, the content of extracting amplitude information from the deep decomposition of the gravity tensor eigenvalues ​​includes: The characteristic equation is constructed from the eigenvalue of the gravity tensor, as shown in Formula 3: Formula 3: ; Where λ is the characteristic solution of the characteristic equation of the gravity tensor, I1 and I2 are the two geometric invariants of the gravity gradient tensor respectively; Let A = -I1 / 3 and B = I2 / 2, and we can get the real root expression of the characteristic equation, as shown in Formula 4: Formula 4: ; By performing a deep decomposition of the gravity tensor eigenvalues, it can be found that each gravity tensor eigenvalue is composed of the same amplitude and different phase angles B.

6. The method for detecting geological structure edges based on enhanced gravity tensor eigenvalues ​​according to claim 1, characterized in that: In step S4, the contents of constructing the enhanced edge detection filter F based on the amplitude information include: Based on the amplitude of the gravity tensor eigenvalue, three-directional derivatives are further obtained, and an enhanced edge detection filter is formed by phase calculation based on the inverse trigonometric function form, and an adjustable parameter p is set to suppress false edge responses; Formula 5: ; Where A = -I1 / 3, C is the improved gravity tensor eigenvalue, and F is the enhanced edge detection filter obtained based on the ratio and phase form, which can be calculated by Formula 3.

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