A geological structure edge detection method based on enhanced gravity tensor eigenvalues

Abstract

This invention relates to the field of geophysics and provides a method for detecting geological structure edges based on enhanced gravity tensor eigenvalues. The method includes: data preparation, generating a gravity gradient tensor matrix; performing eigenvalue decomposition on the gravity gradient tensor matrix to obtain gravity tensor eigenvalues; extracting amplitude information through deep decomposition of the gravity tensor eigenvalues; constructing an enhanced edge detection filter F based on the amplitude information; and, according to the above steps, performing structural edge detection calculations based on enhanced gravity tensor eigenvalues ​​to obtain the edges of underground geological structures. The geological structure (or target body) edge detection results obtained by this invention's method achieve clearer and more continuous recognition results compared to commonly used eigenvalue detection techniques, with higher positioning accuracy. It is also well-suited for characterizing the edges of geological structures (or target bodies) superimposed at multiple scales and different burial depths, making it more practical.

Description

Technical Field

[0001] This invention relates to the field of geophysics, and in particular to a method for detecting geological structural edges based on enhanced gravity tensor eigenvalues. Background Technology

[0002] In the field of geophysical exploration, gravity measurement is an important means of detecting the spatial location and occurrence state of underground 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. Among these technologies, gravity tensor eigenvalues ​​possess high horizontal resolution, providing a powerful tool for interpreting geological body morphology and identifying structural edges or boundary features. However, existing geological structure edge detection methods based on gravity tensor eigenvalues ​​mainly rely on the maximum eigenvalue of the gravity tensor matrix itself. In practical applications, these methods are often affected by the morphology and geometric parameters of geological structures, leading to limited edge location accuracy. Furthermore, they are sensitive to changes in geological body depth, making it difficult to precisely characterize the edge distribution features of geological bodies under different depth conditions. To overcome these limitations, this invention proposes a geological structure edge detection method based on enhanced gravity tensor eigenvalues. This method can significantly improve the edge detection accuracy and resolution in complex geological bodies (especially geological structures or superimposed geological bodies at different depths), thereby effectively improving the accuracy of gravity data interpretation. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the purpose of this 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 art.

[0004] A method for detecting geological structural edges based on enhanced gravity tensor eigenvalues, the method comprising:

[0005] S1. Data preparation, generating the gravity gradient tensor matrix;

[0006] S2. Perform row eigenvalue decomposition on the gravity gradient tensor matrix to obtain the eigenvalues ​​of the gravity tensor;

[0007] S3. Extract amplitude information from the eigenvalues ​​of the gravity tensor through deep decomposition;

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

[0009] S5. Based on steps S1-S4, perform structural edge detection calculation based on enhanced gravity tensor eigenvalues ​​to obtain clear, continuous, and location-reliable underground geological structure (or target body) edges.

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

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

[0012] Formula 1: ;

[0013] In the formula, T xx T xy T xz T yy T yz These are the five independent components of the gravity gradient tensor, based on the passivity of the gravitational field (T). xx 2 +T yy 2 +T zz 2 =0) and irrotational symmetry (T xy = T yx , T xz = T zx , T yz = T zy As can be seen, when the gravity gradient tensor has any 5 independent components, it can be expanded into a complete gravity gradient tensor matrix.

[0014] Preferably, in step S1, if the number of measured components is insufficient in the actual measurement, it is necessary to obtain 5 independent components through gradient component conversion of the gravitational field.

[0015] Preferably, in step S2, the row eigenvalues ​​of the gravity tensor are obtained by performing eigenvalue decomposition on the gravity gradient tensor matrix, including:

[0016] Eigenvalues ​​of the gravity gradient tensor matrix T at each grid point are obtained by performing eigenvalue decomposition, as shown in Equation 2:

[0017] Formula 2: ;

[0018] In the formula, λ i Let v be the eigenvalues ​​of the gravity tensor, λ1>λ2>λ3. i Let i be the eigenvector corresponding to each gravity tensor eigenvalue, i=1, 2, 3. In conventional methods, the largest eigenvalue corresponds to the edge distribution of geological structures, so it is a commonly used 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 eigenvalues ​​of the gravity tensor, as shown in Equation 3:

[0021] Formula 3: ;

[0022] In the formula, λ is the characteristic solution of the gravity tensor characteristic equation, and I1 and I2 are two geometric invariants (also known as tensor invariants) of the gravity gradient tensor.

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

[0024] Formula 4: ;

[0025] By performing a deep decomposition of the eigenvalues ​​of the gravity tensor, it can be seen that each eigenvalue of the gravity tensor is composed of the same amplitude. It consists of different phase angles B, where the amplitude information of the gravity tensor eigenvalues ​​is key to indicating the edge features of geological structures (or target bodies).

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

[0027] Based on the amplitude of the eigenvalues ​​of the gravity tensor, the three-directional derivatives are further obtained, and an enhanced edge detection filter is formed by phase determination based on the inverse trigonometric function form (Formula 5). An adjustable parameter p (empirical range 1-10) is set to suppress false edge responses.

[0028] Formula 5: ;

[0029] In the formula, 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] The beneficial effects achieved by this invention are as follows:

[0031] This invention achieves a high-precision, high-resolution method for detecting geological structure edges by performing deep decomposition of gravity tensor eigenvalues ​​and designing enhanced filters, thus improving the adaptability and reliability of gravity tensor eigenvalue edge detection methods for interpreting complex geological structures. Compared to commonly used eigenvalue detection techniques, the edge detection results of geological structures (or target bodies) obtained by this invention provide clearer and more continuous recognition results, higher positioning accuracy, and are well-suited for characterizing the edges of geological structures (or target bodies) at multiple scales and different burial depths, making it more practical. Attached Figure Description

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

[0033] Figure 2This is a graph of the superimposed gravity gradient full tensor data of a multi-geological-body combination model.

[0034] Figure 3 This is a map showing the geological structure edge detection results of a multi-geological-body combination model.

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

[0036] Figure 5 This is a map showing the results of geological structure edge detection based on measured gravity tensor eigenvalues. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Please see Figure 1 This invention provides a method for detecting geological structural edges based on enhanced gravity tensor eigenvalues, the method comprising:

[0039] S1. Data preparation, generating the gravity gradient tensor matrix;

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

[0041] Formula 1: ;

[0042] In the formula, T xx T xy T xz T yy T yz These are the five independent components of the gravity gradient tensor (if the number of measured components is insufficient in actual measurements, then the five independent components need to be obtained through gradient component transformation of the gravity field), based on the passivity of the gravity field (T). xx 2 +T yy 2 +T zz 2 =0) and irrotational symmetry (T xy = T yx , T xz = T zx , T yz = T zyAs can be seen, when the gravity gradient tensor has any 5 independent components, it can be expanded into a complete gravity gradient tensor matrix.

[0043] S2. Perform row eigenvalue decomposition on the gravity gradient tensor matrix to obtain the eigenvalues ​​of the gravity tensor;

[0044] Eigenvalues ​​of the gravity gradient tensor matrix T at each grid point are obtained by performing eigenvalue decomposition, as shown in Equation 2:

[0045] Formula 2: ;

[0046] In the formula, λ i Let v be the eigenvalues ​​of the gravity tensor, λ1>λ2>λ3. i Let i be the eigenvector corresponding to each gravity tensor eigenvalue, i=1, 2, 3. In conventional methods, the largest eigenvalue corresponds to the edge distribution of geological structures, so it is a commonly used choice for edge detection methods.

[0047] S3. Extract amplitude information from the eigenvalues ​​of the gravity tensor through deep decomposition;

[0048] The characteristic equation is constructed from the eigenvalues ​​of the gravity tensor, as shown in Equation 3:

[0049] Formula 3: ;

[0050] In the formula, I1 and I2 are two geometric invariants of the gravity gradient tensor, respectively;

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

[0052] Formula 4: ;

[0053] By performing a deep decomposition of the eigenvalues ​​of the gravity tensor, it can be seen that each eigenvalue of the gravity tensor is composed of the same amplitude. It consists of different phase angles B, where the amplitude information of the gravity tensor eigenvalues ​​is key to indicating the edge features of geological structures (or target bodies).

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

[0055] Based on the amplitude of the eigenvalues ​​of the gravity tensor, the three-directional derivatives are further obtained, and an enhanced edge detection filter is formed by phase determination based on the inverse trigonometric function form (Formula 5). An adjustable parameter p (empirical range 1-10) is set to suppress false edge responses.

[0056] Formula 5: ;

[0057] In the formula, 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. Based on steps S1-S4, perform structural edge detection calculation based on enhanced gravity tensor eigenvalues ​​to obtain clear, continuous, and location-reliable underground geological structure (or target body) edges.

[0059] A complex model was simulated to verify the reliability and practicality of the method of this invention. The model consists of four rectangular prisms of different sizes and burial depths, mainly considering the actual situation of simultaneous distribution of shallow and deep sections and the superposition of closely spaced geological structures. The observed anomaly data for each component of the gravity gradient tensor generated by this model are as follows: Figure 2 As shown in the figure, the black boxes represent the horizontal position projections corresponding to the edges of each model. By utilizing the gravity gradient tensor components generated by this model, and processing them using both the conventional geological structure edge method based on the maximum gravity tensor eigenvalue and the method of this invention, the following results 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 obtain the geological boundary features of shallowly buried and large-scale models, while 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 edge detection results of the method of this invention have high image resolution, and the identified locations correspond accurately to the actual locations, indicating that the resolution and accuracy of the method are higher than those of conventional methods.

[0060] To further illustrate the applicability and stability of the method, measured gravity gradient full tensor data from the St. George Bay region of Canada were selected. Figure 4 Tests were conducted to obtain the distribution of geological structural boundaries at different scales in the underground space of the region. Figure 5 The left image shows the edge detection results of conventional methods based on the maximum eigenvalue of the gravity tensor, while the right image shows the edge detection results of the method proposed in this invention. In the images, the solid black lines represent the boundaries of known geological structures, and the dashed lines represent the edges of newly discovered concealed geological structures. The enhanced gravity tensor eigenvalue edge detection method proposed in this invention has higher resolution, better matches the locations of known structures, and can also help identify and reveal the boundaries of small-scale or deeply buried concealed geological structures within a region.

[0061] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made using the present invention specification, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for detecting geological structural edges based on enhanced gravity tensor eigenvalues, characterized in that, The method includes: S1. Data preparation, generating the gravity gradient tensor matrix; S2. Perform row eigenvalue decomposition on the gravity gradient tensor matrix to obtain the eigenvalues ​​of the gravity tensor; S3. Extract amplitude information from the eigenvalues ​​of the gravity tensor through deep decomposition; S4. Construct an enhanced edge detection filter F based on the amplitude information; S5. Based on steps S1-S4, perform structural edge detection calculation based on enhanced gravity tensor eigenvalues ​​to obtain the edge of underground geological structures; In step S4, the construction of the enhanced edge detection filter F based on the amplitude information includes: Based on the amplitude of the eigenvalues ​​of the gravity tensor, the three-directional derivatives are further obtained, and an enhanced edge detection filter is formed by phase determination based on the inverse trigonometric function form. An adjustable parameter p is set to suppress false edge responses. Formula 5: ; In the formula, T xx T xy T xz T yy T yz For the five independent components of the gravity gradient tensor; A = -I1 / 3, where I1 are the geometric invariants of the gravity gradient tensor, C is the improved eigenvalue of the gravity tensor, and F is the enhanced edge detection filter obtained based on the ratio and phase form.

2. The geological structure edge detection method based on enhanced gravity tensor eigenvalues ​​according to claim 1, characterized in that, In step S1, data preparation and the generation of the gravity gradient tensor matrix include: Obtain any five independent components of the measured gravity tensor gradient in Cartesian coordinates to form the gravity gradient tensor matrix T, as follows: Formula 1: ; In the formula, T xx T xy T xz T yy T yz These are the five independent components of the gravity gradient tensor. Based on the absence of source and the irrotational symmetry of the gravitational field, it can be known that when any five independent components of the gravity gradient tensor are present, it can be extended into a complete gravity gradient tensor matrix.

3. The geological structure edge detection method 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 5 independent components through gradient component conversion of the gravitational field.

4. The geological structure edge detection method based on enhanced gravity tensor eigenvalues ​​according to claim 1, characterized in that, In step S2, the row eigenvalues ​​of the gravity tensor are obtained by performing eigenvalue decomposition on the gravity gradient tensor matrix. Eigenvalues ​​of the gravity gradient tensor matrix T at each grid point are obtained by performing eigenvalue decomposition, as shown in Equation 2: Formula 2: ; In the formula, λ i Let v be the eigenvalues ​​of the gravity tensor, λ1>λ2>λ3. i Let i be the eigenvectors corresponding to the eigenvalues ​​of each gravity tensor, i=1, 2,3.

5. The geological structure edge detection method 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 eigenvalues ​​of the gravity tensor, as shown in Equation 3: Formula 3: ; In the formula, λ is the characteristic solution of the gravity tensor characteristic equation, and I1 and I2 are two geometric invariants of the gravity gradient tensor. Let A = -I1 / 3 and B = I2 / 2, we can obtain the expression for the real roots of the characteristic equation, as shown in Formula 4: Formula 4: ; By performing a deep decomposition of the eigenvalues ​​of the gravity tensor, it can be seen that each eigenvalue of the gravity tensor is composed of the same amplitude. It consists of different phase angles B.

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