A High-Precision Joint Gravity and Magnetic Property Vector Inversion Method Based on Data Transformation

Abstract

This invention relates to the field of geophysical inversion technology and provides a high-precision joint gravity and magnetic property vector inversion method based on data transformation. The method includes the following steps: Step S1, dividing the subsurface space into N block units in spherical coordinates; Step S2, establishing a kernel function matrix A; Step S3, establishing a regularized inversion objective function; Step S4, constructing Gram constraint terms; Step S5, establishing the objective function for Gram joint property vector inversion; Step S6, iteratively calculating the density and magnetic vector results using the objective function. This invention enhances the correlation between different parameters of the Gram constraint based on data transformation, and utilizes the strengthened constraint relationship for joint gravity and magnetic property vector inversion, thus improving the resolution of the inversion results.

Description

Technical Field

[0001] This invention belongs to the field of geophysical inversion technology, and particularly relates to a high-precision joint gravity and magnetic property vector inversion method based on data transformation. Background Technology

[0002] Subsurface density and magnetic vector structure obtained from gravity and magnetic data inversion are crucial for revealing geological structures and tectonic movements. Some methods that utilize rock physics and geological information, statistical correlations, and other physical properties for constraint require certain prior information. When prior information is insufficient, cross-gradient joint inversion based on structural constraints and Gramme joint inversion based on physical property constraints have become commonly used joint inversion methods. The inversion of the remanent magnetic structure of the lithosphere typically employs magnetic vector inversion. This method is more suitable for inverting magnetic data affected by remanent magnetization, as it can simultaneously calculate magnetic susceptibility and magnetization direction, revealing the spatial distribution of subsurface magnetic structures and thus clearly characterizing the lithosphere structure.

[0003] Gravity and magnetic data are superimposed, making it difficult to separate the various subsurface geological information contained within the data. Therefore, single-data inversion often fails to achieve ideal results. Joint inversion of gravity and magnetic data is based on the correlation of physical properties or the structural coupling relationship of subsurface geological bodies. It inverts different observation data to obtain a unified geophysical model. The choice of constraint method and the strength of the constraint will have different effects on the joint inversion results. Gram matrix constraint, as a generalized constraint, can utilize functional characteristics to transform data and construct new physical property constraints, strengthen the correlation between physical properties to improve the constraint effect, and thus improve the inversion resolution. Summary of the Invention

[0004] The purpose of this invention is to provide a high-precision joint gravity and magnetic property vector inversion method based on data transformation, which aims to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The high-precision joint gravity and magnetic property vector inversion method based on data transformation includes the following steps:

[0007] Step S1: Divide the underground space into N block units in spherical coordinates;

[0008] Step S2: Establish kernel function matrix A;

[0009] Step S3: Establish the objective function for regularization inversion;

[0010] Step S4: Construct Gram constraint terms;

[0011] Step S5: Establish the objective function for Gramm joint property vector inversion;

[0012] Step S6: Iterate and calculate the density and magnetic vector results for the objective function.

[0013] Furthermore, the specific operations include the following:

[0014] The initial density result m was obtained by regularized inversion using gravity and magnetic data. a and the initial magnetic vector result m b ; the initial density result m a and the initial magnetic vector result m b After data transformation, Gram constraint terms are constructed; the objective function of Gram joint property vector inversion based on data transformation is used for iterative calculation to obtain the density result m. c And magnetic vector result m d .

[0015] Furthermore, the objective function of the regularization inversion is:

[0016]

[0017] Where A is the kernel function matrix; d is the observed data; m 1，2 The desired property is represented by α, which is the regularization coefficient and is typically set to 1.

[0018] Furthermore, the Gram constraint term based on data transformation takes the following form:

[0019]

[0020] Where S(Lm1,Lm2) is a Gram constraint term based on data transformation; m1 and m2 represent two different physical properties respectively; L is a data transformation factor, which can be any function.

[0021] Furthermore, the objective function for the Gram joint property vector inversion based on data transformation is:

[0022]

[0023]

[0024] Where A is the established kernel function matrix; d is the observed data; m1 and m2 represent two different physical properties; η1 and η2 represent two different Gram constraint coefficients; α is the regularization coefficient, which is generally taken as 1; L is the data transformation factor, which can be any function.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] This high-precision joint gravity and magnetic property vector inversion method based on data transformation enhances the correlation between different parameters of the Gram constraint by using the enhanced constraint relationship to perform joint gravity and magnetic property vector inversion, thereby improving the resolution of the inversion results. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the specific inversion method of the present invention.

[0028] Figure 2 The figures show the results of gravity anomaly data and magnetic anomaly data in Embodiment 1 of the present invention; where (a) is gravity anomaly data and (b) is magnetic anomaly data.

[0029] Figure 3 This is a diagram showing the model inversion results in Embodiment 1 of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0031] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0032] An embodiment of the present invention provides a high-precision joint gravity and magnetic property vector inversion method based on data transformation, comprising the following steps:

[0033] Step S1: Divide the underground space into N block units in spherical coordinates;

[0034] Step S2: Establish kernel function matrix A;

[0035] Step S3: Establish the objective function for regularization inversion:

[0036]

[0037] Where A is the kernel function matrix; d is the observed data; m 1，2 The desired property is represented by α, which is the regularization coefficient and is typically set to 1.

[0038] Step S4: Construct Gram constraint terms;

[0039] The existing Gram constraints are as follows:

[0040]

[0041] Where S(m1,m2) is a Gram constraint term, and m1 and m2 represent two different physical properties respectively;

[0042] The Gram constraint term based on data transformation proposed in this invention has the following form:

[0043]

[0044] Where S(Lm1,Lm2) is a Gram constraint term based on data transformation; m1 and m2 represent two different physical properties respectively; L is a data transformation factor, which can be any function, such as the arctan function.

[0045] Step S5: Establish the objective function for Gramm joint property vector inversion;

[0046] The existing objective function for Gramm joint property vector inversion is:

[0047]

[0048]

[0049] Where η1 and η2 represent two different Gram constraint coefficients, which determine the relative weights of different physical property coupling terms. Their calculation formulas are as follows:

[0050]

[0051]

[0052] The objective function for Gram joint property vector inversion based on data transformation proposed in this invention is:

[0053]

[0054]

[0055] Where A is the established kernel function matrix; d is the observed data; m1 and m2 represent two different physical properties; η1 and η2 represent two different Gram constraint coefficients; α is the regularization coefficient, which is generally taken as 1; L is the data transformation factor, which can be any function, such as the arctan function.

[0056] Step S6: Iterate and calculate the density and magnetic vector results for the objective function.

[0057] As a preferred embodiment of the present invention, the following operations are specifically included:

[0058] The initial density result m was obtained by regularized inversion using gravity and magnetic data. a and the initial magnetic vector result m b ; the initial density result m a and the initial magnetic vector result m bAfter data transformation, Gram constraint terms are constructed; the objective function of Gram joint property vector inversion based on data transformation is used for iterative calculation to obtain high-precision density result m. c And magnetic vector result m d .

[0059] Example 1: The feasibility of the method was verified through simulation experiments. The simulation process is as follows:

[0060] 1) Simulations yielded gravity anomaly data and magnetic anomaly data, such as... Figure 2 As shown;

[0061] 2) Construct the inversion kernel function matrix;

[0062] 3) The existing regularized inversion method was used for calculation, and the density and magnetic vector results are as follows: Figure 3 As shown in (a) and (b), the black solid lines in the figures represent the location and shape of geological bodies;

[0063] 4) The existing Gramm-constrained inversion method was used for calculation, and the density and magnetic vector results are as follows: Figure 3 As shown in (c) and (d);

[0064] 5) The calculation was performed using a Gram-constrained inversion method based on data transformation. The density and magnetic vector results are as follows: Figure 3 As shown in (e) and (f), the results demonstrate the high resolution of the method of the present invention.

[0065] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A high-precision joint physical property vector inversion method based on data transformation, characterized in that, The method comprises the following steps: Step S1, dividing the underground space into N block units in a spherical coordinate system; Step S2, establishing a kernel function matrix A; Step S3, establishing a target function of the regularization inversion; Step S4, constructing a Galem constraint term; Step S5, establishing a target function of the Galem combined physical property vector inversion; Step S6, iteratively calculating and solving the density result and the magnetic vector result of the target function; The target function of the regularization inversion is: ； wherein, is a kernel matrix; is an observation data; is a sought property; is a regularization coefficient, taken as 1; The Galem constraint term based on data transformation is in the form of: ； wherein, is a glem constraint based on data transformation; respectively represent two different physical properties; is a data transformation factor, which can be an arbitrary function; The target function of the Galem combined physical property vector inversion based on data transformation is: ； ； wherein and respectively represent two different Gram constraints coefficients.

2. The high-precision joint physical property vector inversion method based on data transformation of claim 1, characterized in that, Specifically, the following operations are included: The initial density result m a and the initial magnetic vector result m b are obtained by using the gravity and magnetic data for regularization inversion respectively; the initial density result m a and the initial magnetic vector result m b are subjected to data conversion to construct a Galem constraint term; the objective function of the Galem combined physical property vector inversion based on data conversion is used for iterative calculation to obtain the density result m c and the magnetic vector result m d .

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