CN116794735A - Aviation magnetic vector gradient data equivalent source multi-component joint denoising method and device - Google Patents
Aviation magnetic vector gradient data equivalent source multi-component joint denoising method and device Download PDFInfo
- Publication number
- CN116794735A CN116794735A CN202310648977.3A CN202310648977A CN116794735A CN 116794735 A CN116794735 A CN 116794735A CN 202310648977 A CN202310648977 A CN 202310648977A CN 116794735 A CN116794735 A CN 116794735A
- Authority
- CN
- China
- Prior art keywords
- data
- equivalent source
- model
- magnetic vector
- component
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000013598 vector Substances 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 60
- 239000011159 matrix material Substances 0.000 claims abstract description 30
- 238000007781 pre-processing Methods 0.000 claims abstract description 28
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 13
- 230000000704 physical effect Effects 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 7
- 238000004859 neutralization-reionization mass spectrometry Methods 0.000 claims description 4
- 238000000926 separation method Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 44
- 238000010586 diagram Methods 0.000 description 10
- 230000005415 magnetization Effects 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 230000002159 abnormal effect Effects 0.000 description 6
- 238000009826 distribution Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000004907 flux Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000005358 geomagnetic field Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Abstract
The application discloses an aviation magnetic vector gradient data equivalent source multi-component joint denoising method and device, wherein the method comprises the following steps: obtaining observation data of gradient components of the aviation magnetic vector and preprocessing the observation data to obtain preprocessed data; an equivalent source model is arranged for the preprocessing data; generating a kernel function matrix of the equivalent source model; and separating noise in the preprocessed data based on inversion of the kernel function matrix. The method and the device of the application take the unified consideration of the effective signals of all components in the aviation magnetic vector gradient data as the premise, and simultaneously carry out denoising treatment on all the components, so that the denoising result has higher precision and maintains the relativity among each component, and the denoising result of each component has more definite physical significance.
Description
Technical Field
The application relates to the field of geophysics, in particular to an equivalent source multi-component joint denoising method and device for aviation magnetic vector gradient data.
Background
The aviation magnetic vector gradient data has higher detection efficiency than the traditional bit field data, is less influenced by the geomagnetic field, has rich response magnetic field information and contains more high-frequency signal components. Interpretation based on gradient tensor data can lead to higher resolution and more detailed geologic structures, thus placing new and more stringent demands on the processing of the data.
The aviation magnetic vector gradient data are 9 components obtained by respectively deriving components of geomagnetic vectors in three directions of x, y and z in a space Cartesian rectangular coordinate system, and a gradient tensor matrix can be expressed as follows:
wherein: u is magnetic mark, B x 、B y 、B z The magnetic field intensity is projected on three coordinate axes of a space rectangular coordinate system respectively. According to maxwell's equations, the divergence and rotation of the passive spatial geomagnetic vector field are both zero, i.e., divb=0, rotb=0, so there are 5 completely independent components in the gradient tensor matrix.
B xy ＝B yz ,B xz ＝B zx ,B yz ＝B zy
The method has the advantages that the method starts later in the aspect of aviation magnetic vector gradient detection in China, but related scientific research institutions are gradually developing the research and design of aviation magnetic vector gradient measurement equipment, at present, aviation magnetic vector gradient data cannot be put into actual production, and one very important problem restricting the application of the aviation magnetic vector gradient data is that noise in observed data is large and cannot be effectively removed, so that the research of a set of noise removing methods suitable for aviation magnetic vector gradient data is very important.
Disclosure of Invention
The application provides an equivalent source multi-component joint denoising method and device for aviation magnetic vector gradient data, which are used for solving the problems that in the prior art, the denoising process does not fully utilize information of each component, the processing is more unilateral, the effect is poor, the correlation between the processed result and other components is reduced, the reflected field source has deviation, and unnecessary errors are generated in the application calculation of later aviation magnetic vector gradient data.
The application discloses an aviation magnetic vector gradient data equivalent source multi-component joint denoising method, which comprises the following steps:
obtaining observation data of gradient components of the aviation magnetic vector and preprocessing the observation data to obtain preprocessed data;
an equivalent source model is arranged for the preprocessing data;
generating a kernel function matrix of the equivalent source model;
and separating noise in the preprocessed data based on inversion of the kernel function matrix.
Optionally, the preprocessing includes: compensation, leveling, and downsampling.
Optionally, laying out an equivalent source model for the preprocessing data includes:
setting at least one layer of unit body model; the unit body model is used for simulating underground real geological conditions;
and establishing a relationship between physical property parameters and magnetic anomalies of each layer of unit body model through forward calculation so as to construct the equivalent source model.
Optionally, the method further comprises:
assuming that the unit body model is regularly distributed, physical parameters of the unit body model can be represented by the following formula:
m＝(m 1 ,m 2 ,m 3 ,…,m N-1 ,m N ) T ,
wherein m is N The physical parameters of the Nth unit body model are represented, and N is the number of the unit body models;
and establishing an equation set of the kernel function matrix, the physical property parameters and the preprocessing data.
Optionally, the method further comprises: the unit body model is at least one of the following:
magnetic dipoles, even layers, and cuboids.
Optionally, the depth of the equivalent source model is 5-7 times of the measuring point distance.
Optionally, the method further comprises: acquiring a regularization factor through an L curve method or the signal-to-noise ratio of the preprocessed data; the regularization factor is used to determine a difference of fit and a complexity of the equivalent source model.
Optionally, the method further comprises: and evaluating the accuracy of the denoising data reconstructed by the equivalent source model by using the normalized mean square error NRMS.
The application relates to an aviation magnetic vector gradient data equivalent source multi-component joint denoising device, which comprises:
the preprocessing unit is used for acquiring the observation data of the gradient component of the aviation magnetic vector and preprocessing the observation data to obtain preprocessed data;
the first processing unit is used for laying out an equivalent source model for the preprocessing data;
the second processing unit is used for generating a kernel function matrix of the equivalent source model;
and the noise separation unit is used for separating noise in the preprocessed data based on inversion of the kernel function matrix.
A computer readable storage medium of the present application stores one or more programs executable by one or more processors to implement the steps of the aero magnetic vector gradient data equivalent source multicomponent joint denoising method as described in any one of the above.
According to the application, on the premise that effective signals of all components in the aviation magnetic vector gradient data are uniformly considered, denoising processing is carried out on all the components simultaneously, so that the denoising result has higher precision and correlation among each component is kept, and the denoising results of all the components have more definite physical significance.
Drawings
FIG. 1 is a flow chart of an airborne magnetic vector gradient data equivalent source multi-component joint denoising method in an embodiment of the present application;
FIG. 2 is a schematic diagram of equivalent source model modeling in an embodiment of the application;
FIG. 3 is a schematic diagram of a rectangular parallelepiped model of a north-east coordinate system in an embodiment of the application;
FIG. 4 is a schematic diagram of determining regularization factors in an embodiment of the application;
FIG. 5 is a block diagram of an equivalent source multicomponent joint denoising apparatus for aeromagnetic vector gradient data in an embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
It should be understood that, in the various embodiments herein, the sequence number of each process described above does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments herein.
The embodiment of the application provides an aviation magnetic vector gradient data equivalent source multi-component joint denoising method, which is shown in fig. 1, and comprises the following steps:
and 100, acquiring observation data of gradient components of the aviation magnetic vector and preprocessing to obtain preprocessed data. Specifically, the observed data needs to be preprocessed before the simulation of the equivalent source model. Wherein, the observed data is also measured data. In a preferred embodiment, the preprocessing includes one or more of compensation, leveling, and downsampling.
And 200, laying out an equivalent source model for the preprocessing data. In order to ensure homology among the gradient components of the aviation magnetic vector and preserve the correlation signal relationship and physical significance among the components, an equivalent source method is adopted in the embodiment of the application to lay an equivalent source model for the preprocessed data of the observed data of the gradient components of the aviation magnetic vector after preprocessing, so that the equivalent source model can realize effective, stable and high-precision spatial reconstruction and conversion of the data of the gradient field of the magnetic vector, and further realize the denoising purpose. Specifically, the theoretical basis of the equivalent source model is the gaussian flux theorem, i.e. the normal integral of the bit function of the field source along the closed curved surface surrounding the field source is related to the total amount of field source contained in the curved surface, and is irrelevant to the field source distribution. Therefore, any 'equivalent' field source can be constructed and an equivalent source model can be formed on the premise of meeting the condition that the bit field is unchanged.
And 300, generating a kernel function matrix of the equivalent source model. Specifically, according to the bit field superposition principle, the abnormal value of the magnetic vector gradient component of each observation point should be the sum of the abnormal values generated by the unit body model of all the equivalent sources on the observation point, that is, the abnormal value of the preprocessed data of each observation point after the preprocessing of the magnetic vector gradient component of each observation point in the embodiment of the application is the sum of the abnormal values generated by the unit body model of the equivalent sources on the observation point. And the sum of the abnormal values of the magnetic vector gradient components generated by the unit body model of all the equivalent sources at each observation point is the same as the observation value according to the definition of the equivalent source method. Based on the forward kernel function matrix G of the equivalent source model is established.
Step 400, separating noise in the pre-processed data based on inversion of the kernel function matrix. Specifically, a linear equation set is established between the preprocessed data and the equivalent source model parameters as a basis for inversion calculation. Equivalent source model parameters include magnetization, etc. Because of the non-uniqueness of the solutions of the system of equations, constraints are typically imposed during inversion to reduce multi-solvability.
According to the method provided by the embodiment of the application, a set of reasonable equivalent source models is solved by fitting the effective signals in each aviation magnetic vector gradient component, so that the effective, stable and high-precision spatial reconstruction and conversion of magnetic vector gradient field data are realized, and the purpose of denoising is further realized.
The method for denoising the aviation magnetic vector gradient data equivalent source multi-component combination according to the specific embodiment of the application preferably comprises the following steps of:
setting at least one layer of unit body model; the unit body model is used for simulating underground real geological conditions.
And establishing a relationship between physical property parameters and magnetic anomalies of each layer of unit body model through forward calculation so as to construct the equivalent source model. The magnetic anomaly is caused by the magnetic difference of an underground anomaly body, and a mathematical physical relationship between physical parameters of the anomaly body and the anomaly correspondence is established through forward calculation, so that a foundation can be laid for inverting the equivalent source physical distribution.
Since the observed field distribution is often finite and discrete, reasonably constructing an equivalent source model becomes a key issue for the equivalent source method to achieve high-precision computation results in application. In the embodiment of the application, the equivalent source method is to set one or more layers of unit body models to replace the underground real geological condition, and the inversion calculation method is used to determine the physical parameters of each unit body model so that the physical parameters can fit the observed data, so that the set one or more layers of unit body models are called as equivalent sources. According to the gaussian flux theorem, the normal integral of the bit function of the field source along a closed curved surface surrounding the field source is related to the total amount of field source contained by the curved surface, and is independent of the field source distribution. Therefore, any "equivalent" field source, i.e., equivalent source model, can be constructed with the bit field unchanged satisfied, as shown in FIG. 2.
The embodiment of the application discloses an aeromagnetic vector gradient data equivalent source multi-component joint denoising method, which preferably comprises the following steps:
assuming that the unit body model is regularly distributed, physical parameters of the unit body model can be represented by the following formula:
m＝(m 1 ,m 2 ,m 3 ,…,m N-1 ,m N ) T ,
wherein m is N The physical parameters of the Nth unit body model are represented, and N is the number of the unit body models;
and establishing an equation set of the kernel function matrix, the physical property parameters and the preprocessing data.
Specifically, it is assumed that the observed data of the gradient component of the aeromagnetic vector is preprocessed, and the preprocessed data generated is located at irregular positions or regularly spaced points in the three-dimensional space, so that the algorithm is practical and generally applicable by constructing an equivalent source model using the assumption. Suppose that observed data d can be expressed as:
where p is the number of observations. The actual data amount is k=p×n c ，n c Is the number of components to be processed simultaneously. The six components in the magnetic vector gradient are adopted for construction calculation, and the physical parameter m of the equivalent source unit model can be expressed by the above equation assuming that the unit model of the equivalent source is regularly distributed under the ground.
Specifically, the equation set for establishing the physical property parameters and the preprocessing data of the unit body model based on the forward kernel function matrix G is as follows:
written in matrix form, namely:
d M ＝G M×N m N
wherein: d, d M The vector is the vector of M-dimensional bit field preprocessing data, M is the vector of N-dimensional equivalent source physical parameters, and G is an M multiplied by N-dimensional forward kernel function matrix, which is also called a sensitivity matrix.
Geophysical inversion is a method theory for estimating the distribution and variation of geophysical parameters (geophysical model) of the medium within the earth using observed spatial geophysical field data. The equivalent source physical inversion theory is based on forward modeling.
Let the observed data be d, the physical property parameter be m, and the forward kernel matrix be G. When the number of the observed data is equal to or more than the number of the equivalent sources, the method is called an adaptive and overdetermined problem; the simplest, most common inversion method is the least squares method, i.e. at l 2 Establishing an objective function phi in the norm sense as the variance between the observed data d and the model calculated value, namely:
φ＝(d-Gm) T (d-Gm)
solving the objective function phi to be extremely small, and obtaining a linear equation set:
(G T G)m＝G T d
when the number of equivalent sources is more than the number of observed data, the method is an underdetermined problem. The observed data cannot provide enough information to determine model parameters, and prior information needs to be added to supplement the model parameters, including the value range of physical parameters, other known geological or geophysical data, weight ratios among different data and the simplest model commonly used in underdetermined problem solutions. The "simplest" model refers to a simplification of the geophysical model while retaining the basic features of the actual geophysical model. From l 2 The norm defines the simplest model and establishes an objective function according to extremum theory:
φ＝m T m+ε(d-Gm)
wherein ε= (GG) T ) -1 d, obtaining an extremely small objective function, and obtaining a linear equation set:
m＝G T (GG T ) -1 d
in fact, most geophysical inversion problems are neither fully overdetermined nor fully underdetermined, and appear as a mixed form where the objective function uses a linear combination of overdetermined and underdetermined problem objective functions:
φ＝(d-Gm) T (d-Gm)+μm T m
wherein the first term on the right side is called a data objective function, and the second term is called a model objective function; μ is Tikhonov regularization factor. In the preferred embodiment of the application, the objective function is established on the basis of the above formula, the model objective function is improved by adding constraint conditions such as a reference model, a weighting function, a minimum model, a smoothest model, a physical parameter value range, an interface position and the like into the model objective function, the equivalent source parameters are inverted by fitting effective signals in each observation component, namely, the parameters of each unit body in the established equivalent source model are solved by utilizing the abnormal value of the magnetic vector gradient component of each observation point, the objective of reconstructing the magnetic vector gradient data is achieved by solving the minimization problem, and the inversion objective function is rewritten as follows:
wherein: μ is a regularization factor for determining the difference in data fit and the complexity of the constructed equivalent source; phi is an objective function, phi d Fitting a difference function, phi, to the data m Is a model objective function. The polynomials in the inversion can be suppressed by introducing constraint information, the integral form of which is expressed as:
in the above formula, the first term and the last term on the right side respectively represent the minimum model objective function and the smoothest model objective function, v is the integral area of each discrete equivalent unit, a i The value of the (i=s, x, y) coefficient is used to weigh the weights between the items, w is a depth weighting function, w i (i=s, x, y) is a weight coefficient corresponding to each equivalent unit, and the former term and the latter term control the spatial distribution difference of equivalent source physical properties in whole and in part, respectively.
Discretizing the objective function can obtain:
m in the above 0 As a reference model, it is usually set to 0 in the absence of a priori information; w (W) d For diagonal matrix, diagonal element 1/ε i ；W m The model weighting matrix consists of a weight coefficient diagonal matrix and a differential matrix in the previous formula, and a structure of an equivalent source model is defined.
Specifically, due to the noise in the observed data, instability of the knowledge, also called a disease state problem, is increased, so that the regularization idea can be utilized from the mathematical perspective to improve. Denoising with an equivalent source model is to select appropriate regularization parameters to extract the effective signals from the observed data.
As described above, the inversion objective function includes fitting difference information and model constraint information for the observed data, and partial differentiation of m is performed and is equal to zero, so that it is obtained:
and solving the linear equation set by using an optimization inversion algorithm to obtain the equivalent source model physical parameters which accord with the data fitting difference and the model constraint condition.
The embodiment of the application discloses an aeromagnetic vector gradient data equivalent source multi-component joint denoising method, which preferably comprises the following steps: the unit body model is at least one of the following:
magnetic dipoles, even layers, and cuboids. Specifically, the types of unit models of equivalent sources can be roughly classified into three categories, namely magnetic dipoles, even layers and cuboids. The magnetic dipole is a unit model by utilizing the dipole, and has the advantages of simple calculation and flexible setting, but for a discrete model, the middle of an adjacent field source is difficult to be constrained; the unit body model using the even level as an equivalent source has the advantages that the calculation is relatively simple, but the even level is irrelevant to the actual physical characteristics of the observed data, and the effect of horizontal magnetization in low latitude areas is required to be further analyzed; the unit body model adopting the cuboid as the equivalent source is relatively complex in calculation, but the volume of the unit body model can be adjusted by changing any parameter of the length, the width and the height of the unit body model, which are usually arranged to be closely connected, can be set by referring to the distance between measuring points, and by comparing the advantages and disadvantages of the three types of unit bodies, the embodiment of the application selects the cuboid model which is more in line with the actual geological condition, has a slower attenuation characteristic and can absorb more long wavelength information to lay the equivalent source model.
Specifically, for the magnetic vector gradient component, an equivalent source layer is arranged and split into a plurality of cuboid models to replace the underground real geological condition. As shown in fig. 3, the second derivative of the attraction position of the cuboid is calculated under the North-East coordinate system (namely North East Down, NED coordinate system), then the three components of the magnetic field are calculated according to poisson equation, and the directional derivative is further calculated, so that the calculation formula of the full tensor magnetic gradient of the vertical cuboid upper semi-passive space magnetic field without analytic singular points is as follows:
wherein: b (B) xx ，B xy ，B xz ，B yy ，B yz ，B zz Is the magnetic vector gradient component, unit T/m; m is magnetization intensity, unit A/M; m is M H As a magnetization horizontal component; mu (mu) 0 Permeability in vacuum, mu 0 ＝4π×10 -7 H/m; (x, y, z) is the coordinates of grid points on the observation plane,(ζ, eta, ζ) is the coordinate of the cuboid field source point, and the corresponding integral limit variation range is (ζ) 1 -x,ξ 2 -x),(η 1 -y,η 2 -y),(ζ 1 -z,ζ 2 Z), I is the magnetization tilt, a is the magnetization bias, and is the magnetization horizontal component and the x-axis (i.eGeographic north direction), the angle from the positive x-axis direction to the horizontal magnetization component in the clockwise direction is positive, and vice versa; l=cosicosa, m=cosisina, n=sini, which are the 3-directional cosine of the magnetization.
In the preferred embodiment, it is not possible to find a unique "true" solution due to the non-uniqueness and instability of the solution, and only the unique optimal solution in a sense can be found. Therefore, an "inversion objective function" needs to be established under an acceptable error standard, and a set of model parameters corresponding to the minimum or maximum objective function is the only optimal solution in a certain sense.
According to the aviation magnetic vector gradient data equivalent source multi-component joint denoising method disclosed by the embodiment of the application, the depth of an equivalent source model is preferably 5-7 times of the measuring point distance.
The embodiment of the application discloses an aeromagnetic vector gradient data equivalent source multi-component joint denoising method, which preferably comprises the following steps: and obtaining a regularization factor through an L curve method or the signal-to-noise ratio of the preprocessed data. The regularization factor is used to determine a difference of fit and a complexity of the equivalent source model.
In a preferred embodiment, the regularization parameters are determined using an L-curve method. When the regularization factor is large, the equivalent source model is smooth, and low-frequency components in the observed signals are mainly reflected at the moment; as the regularization factor decreases, the model tends to be not smooth, and the fitting accuracy increases, at which time the high frequency component is gradually added on the low frequency background. When gradient data is actually processed, noise contained in each observation parameter is different, a proper regularization factor is selected, and the low-frequency component in each component can be extracted by denoising by using an equivalent source method. Fig. 4 is a process for determining a regularization factor for data in which the model is built to contain noise, where a suitable regularization factor is a turning point where the model norm increases dramatically, but the fitting norm changes less.
The embodiment of the application discloses an aeromagnetic vector gradient data equivalent source multi-component joint denoising method, which preferably comprises the following steps: and evaluating the accuracy of the denoising data reconstructed by the equivalent source model by using the normalized mean square error NRMS.
Specifically, an equivalent source layer is established by using a proper regularization factor which is screened, and data of a denoising reconstructed magnetic vector gradient component is obtained through forward calculation. And evaluating the data precision reconstructed by the equivalent source model by using the normalized mean square error NRMS.
In B of n To denoise the data of the gradient component of the magnetic vector before reconstruction by using the equivalent source model, B' n And denoising the reconstructed data of the magnetic vector gradient component. B (B) max 、B min The maximum value and the minimum value of the magnetic vector gradient component before denoising reconstruction are respectively.
The embodiment of the application also provides an aviation magnetic vector gradient data equivalent source multi-component joint denoising device, as shown in fig. 5, which comprises:
a preprocessing unit 501, configured to obtain observation data of gradient components of an aeromagnetic vector and perform preprocessing to obtain preprocessed data;
a first processing unit 502, configured to lay out an equivalent source model for the preprocessing data;
a second processing unit 503, configured to generate a kernel function matrix of the equivalent source model;
a noise separation unit 504, configured to separate noise in the preprocessed data based on inversion of the kernel function matrix.
In the preferred embodiment of the application, in order to verify and test the effect of the aviation magnetic vector gradient data equivalent source multi-component joint denoising method, the effect is calculated by establishing a model. The range of the measuring area is 0-20000 m in both the transverse and longitudinal ranges, a cuboid magnetic anomaly is established, and x:6000 to 14000m, y: 8000-12000 m, the buried depth is 2500-3500 m, the magnetic susceptibility is 0.1SI, the magnetic dip angle is 50 degrees, and the magnetic bias angle is 30 degrees. And forward calculating the magnetic vector gradient of the model by utilizing the component formulas of the rectangular magnetic vector gradient. Then, gaussian noise (gaussian noise of acanthopanax percent in terms of difference between maximum value and minimum value of each component) is added to each component of the magnetic vector gradient, where the noise added to each component is not uniform.
An equivalent source model taking a cuboid as a unit body is laid, a kernel function matrix is established by utilizing cuboid magnetic gradient vector forward data (plus noise) according to the steps, parameters of the equivalent source unit body model are inverted, an equivalent source layer is constructed by solving a minimization problem, and a regularization factor is determined by an L curve method;
when the regularization factor is large, the equivalent source model is smooth, and at the moment, the equivalent source model mainly represents low-frequency components in the observed signal; as the regularization factor decreases, the model tends to be unsmooth, and the fitting accuracy increases, i.e., high frequency components are gradually added on the low frequency background. A suitable regularization factor is a sharp increase in model norms, fitting turning points with less norms changing. An appropriate regularization factor is selected. Using an appropriate regularization factor, the data fitting difference and the equivalent source complexity of the construction are determined. And then carrying out equivalent source inversion on six components of the magnetic vector gradient simultaneously, carrying out denoising reconstruction, and separating effective signals and noise. Wherein B is xx The normalized mean square error between the result after component denoising and the theoretical value is 0.9207%, B xy The normalized mean square error of the component and the theoretical value is 0.7998 percent, B xz The normalized mean square error of the component and the theoretical value is 0.6796 percent, B yy The normalized mean square error of the component and the theoretical value is 0.8069 percent, B yz The normalized mean square error of the component and the theoretical value is 0.7035 percent, B zz The normalized mean square error of the component to the theoretical value is 0.9350%. In addition, both the separated noise and the initially added noise are very consistent in detail.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors, so as to implement the steps of the airborne magnetic vector gradient data equivalent source multi-component joint denoising method according to any one of the embodiments above.
By the method in the embodiment of the application, a set of reasonable equivalent source models is solved by fitting the effective signals in each magnetic vector gradient observation component, so that the effective, stable and high-precision spatial reconstruction and conversion of the magnetic vector gradient field data are realized, and the denoising purpose is further realized. Firstly, the accuracy of the single-component denoising result is higher; and secondly, all components after denoising still come from the same bit field, namely, the correlation between each component is still maintained, so that the denoising results of the components have more definite physical significance. The accuracy of the single-component denoising result is higher; and secondly, all components after denoising still come from the same bit field, namely, the correlation between each component is still maintained, so that the denoising results of the components have more definite physical significance.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present application are presented for purposes of illustration and description. It is not intended to limit the application to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain the specific principles of the application and its practical application to thereby enable one skilled in the art to make and utilize the application in various exemplary embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the application be defined by the claims and their equivalents.
Claims (10)
1. An aeromagnetic vector gradient data equivalent source multi-component joint denoising method, which is characterized by comprising the following steps:
obtaining observation data of gradient components of the aviation magnetic vector and preprocessing the observation data to obtain preprocessed data;
an equivalent source model is arranged for the preprocessing data;
generating a kernel function matrix of the equivalent source model;
and separating noise in the preprocessed data based on inversion of the kernel function matrix.
2. The method for removing noise from aviation magnetic vector gradient data equivalent source multi-component combination according to claim 1, wherein the preprocessing comprises: compensation, leveling, and downsampling.
3. The method for performing equivalent source multi-component joint denoising on aeromagnetic vector gradient data according to claim 1, wherein the step of laying out an equivalent source model on the preprocessed data comprises the steps of:
setting at least one layer of unit body model; the unit body model is used for simulating underground real geological conditions;
and establishing a relationship between physical property parameters and magnetic anomalies of each layer of unit body model through forward calculation so as to construct the equivalent source model.
4. An aeromagnetic vector gradient data equivalent source multi-component joint denoising method according to claim 3, further comprising:
assuming that the unit body model is regularly distributed, physical parameters of the unit body model can be represented by the following formula:
m＝(m 1 ,m 2 ,m 3 ,…,m N-1 ,m N ) T ,
wherein m is N The physical parameters of the Nth unit body model are represented, and N is the number of the unit body models;
and establishing an equation set of the kernel function matrix, the physical property parameters and the preprocessing data.
5. An aeromagnetic vector gradient data equivalent source multi-component joint denoising method according to claim 3, further comprising: the unit body model is at least one of the following:
magnetic dipoles, even layers, and cuboids.
6. The method for removing noise by combining multiple components of an equivalent source of aeromagnetic vector gradient data according to claim 1, wherein the depth of the equivalent source model is 5-7 times of the measuring point distance.
7. The method of airborne magnetic vector gradient data equivalent source multi-component joint denoising according to claim 5, further comprising: acquiring a regularization factor through an L curve method or the signal-to-noise ratio of the preprocessed data; the regularization factor is used to determine a difference of fit and a complexity of the equivalent source model.
8. The method of airborne magnetic vector gradient data equivalent source multi-component joint denoising according to claim 1, further comprising: and evaluating the accuracy of the denoising data reconstructed by the equivalent source model by using the normalized mean square error NRMS.
9. An aeromagnetic vector gradient data equivalent source multi-component joint denoising device, which is characterized by comprising:
the preprocessing unit is used for acquiring the observation data of the gradient component of the aviation magnetic vector and preprocessing the observation data to obtain preprocessed data;
the first processing unit is used for laying out an equivalent source model for the preprocessing data;
the second processing unit is used for generating a kernel function matrix of the equivalent source model;
and the noise separation unit is used for separating noise in the preprocessed data based on inversion of the kernel function matrix.
10. A computer readable storage medium storing one or more programs executable by one or more processors to implement the steps of the airborne magnetic vector gradient data equivalent source multicomponent joint denoising method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310648977.3A CN116794735B (en) | 2023-06-02 | 2023-06-02 | Aviation magnetic vector gradient data equivalent source multi-component joint denoising method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310648977.3A CN116794735B (en) | 2023-06-02 | 2023-06-02 | Aviation magnetic vector gradient data equivalent source multi-component joint denoising method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116794735A true CN116794735A (en) | 2023-09-22 |
| CN116794735B CN116794735B (en) | 2024-03-19 |
Family
ID=88045590
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310648977.3A Active CN116794735B (en) | 2023-06-02 | 2023-06-02 | Aviation magnetic vector gradient data equivalent source multi-component joint denoising method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116794735B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090287464A1 (en) * | 2007-01-30 | 2009-11-19 | Arxex Limited | Gravity survey data processing |
| US20140081595A1 (en) * | 2011-03-21 | 2014-03-20 | Arkex Limited | Gravity gradiometer survey techniques |
| CN111856599A (en) * | 2020-06-29 | 2020-10-30 | 中国地质大学（武汉） | Magnetic measurement data equivalent source pole and type conversion method based on PDE |
| CN115201927A (en) * | 2022-07-25 | 2022-10-18 | 中国冶金地质总局山东正元地质勘查院 | Method for heavy magnetic three-dimensional joint inversion based on cross gradient constraint |
| CN116090283A (en) * | 2022-11-11 | 2023-05-09 | 吉林大学 | Aviation electromagnetic three-dimensional inversion method based on compressed sensing and preconditioned random gradient |
-
2023
- 2023-06-02 CN CN202310648977.3A patent/CN116794735B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090287464A1 (en) * | 2007-01-30 | 2009-11-19 | Arxex Limited | Gravity survey data processing |
| CN101600975A (en) * | 2007-01-30 | 2009-12-09 | 阿克斯有限责任公司 | The gravity survey data processing |
| US20140081595A1 (en) * | 2011-03-21 | 2014-03-20 | Arkex Limited | Gravity gradiometer survey techniques |
| CN111856599A (en) * | 2020-06-29 | 2020-10-30 | 中国地质大学（武汉） | Magnetic measurement data equivalent source pole and type conversion method based on PDE |
| CN115201927A (en) * | 2022-07-25 | 2022-10-18 | 中国冶金地质总局山东正元地质勘查院 | Method for heavy magnetic three-dimensional joint inversion based on cross gradient constraint |
| CN116090283A (en) * | 2022-11-11 | 2023-05-09 | 吉林大学 | Aviation electromagnetic three-dimensional inversion method based on compressed sensing and preconditioned random gradient |
Non-Patent Citations (2)
| Title |
|---|
| 勇晓宇: "多源等效层的高精度重磁数据处理方法研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》, no. 1, pages 011 - 388 * |
| 赵德军 等: "基于正则化等效源模型的航空重力梯度测量信息降噪方法", 《中国惯性技术学报》, vol. 29, no. 1, pages 69 - 76 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116794735B (en) | 2024-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bhattacharyya et al. | Reduction of magnetic and gravity data on an arbitrary surface acquired in a region of high topographic relief | |
| Čuma et al. | Large‐scale 3D inversion of potential field data | |
| US6278948B1 (en) | Method for gravity and magnetic data inversion using vector and tensor data | |
| Ren et al. | Gravity gradient tensor of arbitrary 3D polyhedral bodies with up to third-order polynomial horizontal and vertical mass contrasts | |
| CN111856599B (en) | Magnetic measurement data equivalent source pole and type conversion method based on PDE | |
| US20140129194A1 (en) | Methods of three-dimensional potential field modeling and inversion for layered earth models | |
| Pilkington et al. | Potential field continuation between arbitrary surfaces—Comparing methods | |
| Vatankhah et al. | An Efficient Alternating Algorithm for the Lₚ-Norm Cross-Gradient Joint Inversion of Gravity and Magnetic Data Using the 2-D Fast Fourier Transform | |
| CN109856689B (en) | Noise suppression processing method and system for superconducting aeromagnetic gradient tensor data | |
| Martinez et al. | Denoising of gravity gradient data using an equivalent source technique | |
| US20220350042A1 (en) | Method and system for super resolution least-squares reverse time migration | |
| CN112363236A (en) | Gravity field data equivalent source continuation and data type conversion method based on PDE | |
| CN108984939A (en) | Three-dimensional Gravity field of force forward modeling method based on 3D Gauss-FFT | |
| WO2013052035A1 (en) | Methods and systems for multiple-domain inversion of collected data | |
| US20130085731A1 (en) | Methods and systems for multiple-domain inversion of collected data | |
| AU2017241341A1 (en) | Determining displacement between seismic images using optical flow | |
| Schmdit et al. | IGMAS+ a new 3D Gravity, FTG and Magnetic Modeling Software. | |
| Zhou et al. | 2.5-D frequency-domain seismic wave modeling in heterogeneous, anisotropic media using a Gaussian quadrature grid technique | |
| Qin et al. | Three integrating methods for gravity and gravity gradient 3-D inversion and their comparison based on a new function of discrete stability | |
| Mastellone et al. | Volume Continuation of potential fields from the minimum-length solution: An optimal tool for continuation through general surfaces | |
| Ye et al. | Comparison of methods for a 3-D density inversion from airborne gravity gradiometry | |
| Liang et al. | Scattering pattern analysis and true‐amplitude generalized Radon transform migration for acoustic transversely isotropic media with a vertical axis of symmetry | |
| Oliveira Jr et al. | Estimation of the total magnetization direction of approximately spherical bodies | |
| CN116794735B (en) | Aviation magnetic vector gradient data equivalent source multi-component joint denoising method and device | |
| CN112346139A (en) | Gravity data multilayer equivalent source continuation and data conversion method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |