Multi-height aeromagnetic anomaly joint inversion weight factor calculation method

Abstract

A multi-height aeromagnetic anomaly joint inversion weight factor calculation method belongs to the technical field of aeromagnetic exploration, and comprises the following steps: a data acquisition module acquires magnetic anomaly data and coordinates of n layers of set heights of an ore body area; the image drawing module draws a plane contour map of the magnetic anomaly data of each layer according to the magnetic anomaly data and the coordinates thereof; a weight calculation module intercepts observation data with the same XY coordinates from the grid data of each layer of height to form an observation data matrix of each layer of height; a weight calculation module calculates to obtain a weight factor corresponding to each layer of height aeromagnetic anomaly according to the observation data matrix; and the inversion calculation module constructs a joint inversion target equation of ore body distribution according to the weight factors, and performs inversion iterative calculation to obtain ore body distribution state data of the ore body area. The method has the advantages that the applicability is high, the weight factor can be directly generated according to the overall energy and the strongest feature of different height data, repeated trial and error are avoided, and the inversion efficiency can be greatly improved.

Description

Technical Field

[0001] This invention relates to the field of airborne magnetic exploration technology, and in particular to a method for calculating the weighting factor of joint inversion of multi-altitude airborne magnetic anomalies. Background Technology

[0002] Aeromagnetic anomalies at different altitudes contain subsurface spatial information at varying depths and scales. For example, data from low altitudes are primarily dominated by shallow, small-scale anomalies, while data from high altitudes are mainly dominated by deep, large-scale anomalies. Joint inversion can simultaneously characterize anomaly information at different depths and scales. However, in multi-altitude aeromagnetic anomaly joint inversion, because the intensity of magnetic anomalies decreases rapidly with increasing distance, the amplitude of data from low altitudes is much greater than that from high altitudes. This results in low-altitude data dominating the inversion, while the influence of high-altitude data on the inversion results is negligible. Therefore, it is necessary to assign weights to data from different altitudes to ensure their contribution to the inversion is roughly consistent. Previous studies have primarily employed a trial-and-error approach to selecting weighting factors, i.e., setting a series of values ​​and performing multiple inversion calculations on data from each altitude, ultimately selecting the one with the best performance.

[0003] Disadvantages of existing technologies: Existing methods for calculating inversion weight factors require setting a series of values ​​and repeated trial and error, resulting in drawbacks such as large computational load, low inversion efficiency, and time and effort consumption. Summary of the Invention

[0004] The present invention provides a method for calculating weighting factors in the joint inversion of aeromagnetic anomalies at multiple altitudes. This method has high applicability and can directly generate weighting factors based on the overall energy and strongest features of data at different altitudes, avoiding repeated trial and error and greatly improving inversion efficiency.

[0005] To achieve the above objectives, this invention provides a method for calculating the weighting factor in the joint inversion of multi-altitude aeromagnetic anomalies, the key of which includes the following steps:

[0006] Step 1: Construct a multi-altitude aeromagnetic anomaly joint inversion weight factor calculation system. This multi-altitude aeromagnetic anomaly joint inversion weight factor calculation system is equipped with a data acquisition module, an image rendering module, a weight calculation module, and an inversion calculation module connected in sequence.

[0007] Step 2: The data acquisition module collects magnetic anomaly data at a set height in n layers of the ore body area, as well as the coordinates corresponding to each magnetic anomaly data;

[0008] Step 3: The image rendering module acquires the magnetic anomaly data and its corresponding coordinates, then performs gridding based on the coordinates, and draws a planar contour map of the magnetic anomaly data at each layer height;

[0009] Step 4: The weight calculation module selects observation data with the same XY coordinates (i.e., magnetic anomaly data) from the grid data of each layer based on the plane contour map of each layer, and constructs the observation data matrix for each layer. , ;

[0010] Step 5: The weight calculation module calculates the weights based on the observed data matrix. The observation data matrix corresponding to each layer height was calculated separately. Energy weighting coefficient at this height of overall energy and the feature weighting coefficient on the strongest feature at that height ;

[0011] Step 6: The weight calculation module calculates the weights based on the energy weighting coefficients. and feature weighting coefficients The weighting factors corresponding to the aeromagnetic anomalies at each altitude were calculated separately. ;

[0012] Step 7: The inversion calculation module calculates the weighting factors. A joint inversion objective equation for the distribution of ore bodies is constructed, and inversion iterative calculations are performed to obtain the ore body distribution status data of the ore body region.

[0013] Through the above design, magnetic anomaly data and their coordinates at different altitudes are collected, and corresponding planar contour maps are drawn. Then, observation data with the same coverage area (i.e., different altitudes but the same XY coordinates) are extracted from each planar contour map. An observation data matrix for each altitude level is then constructed, and the energy weighting coefficient and feature weighting coefficient corresponding to the observed data vector are calculated. Finally, the weighting factor for each altitude level is obtained. After obtaining the weighting factors, a joint inversion objective equation for the orebody distribution is constructed based on the weighting factors, and inversion iterative calculations are performed to obtain the orebody distribution status data for the orebody area. This orebody distribution status data reflects the mineral distribution in the orebody area and can be used for mineral exploration.

[0014] This invention only needs to directly generate the corresponding weight factor based on the overall energy and strongest feature of data at different heights, avoiding repeated trial and error in the weight selection process and greatly improving the inversion efficiency.

[0015] Preferably, the data acquisition module is a drone equipped with a magnetic probe and a GPS probe.

[0016] In step 2, the UAV flies at different set altitudes. The magnetic probe on the UAV is used to collect magnetic anomaly data of the ore body area at the corresponding altitude, and the GPS probe on the UAV is used to collect the coordinates and altitude information of each observation point.

[0017] In airborne magnetic surveys, magnetic anomaly data for anomalies at all depths can be collected at different altitudes, but the corresponding data resolutions differ. This is because the attenuation of magnetic anomalies is positively correlated with distance and anomaly size: at higher altitudes, the attenuation rate of shallow, small-scale anomalies is high, primarily preserving information about deep, large-scale anomalies; at lower altitudes, magnetic anomalies are mainly dominated by shallow anomalies.

[0018] This invention allows the drone to fly at different altitudes, enabling the magnetic probe on the drone to collect data at different depths based on the flight altitude; at the same time, the GPS probe collects the coordinates and altitude information of each observation point.

[0019] Preferably, in step 5, the weighting calculation module calculates the energy weighting coefficients for each layer height. The calculation expression is:

[0020] ;

[0021] ;

[0022] in, Indicates the first The root mean square of the layer height observation data matrix, ; This indicates the total number of observation data in the current layer. Indicates the current layer's first... One observation data, ; Indicates the first Energy weighting coefficient for floor height.

[0023] The weighting calculation module calculates the energy weighting coefficient for each layer based on the root mean square of the observation data for each layer.

[0024] Preferably, in step 5, the weight calculation module calculates the feature weighting coefficients for each layer height. The calculation process is as follows:

[0025] First, the Gram matrix of the observation data matrix for each layer is calculated, as shown in the following expression:

[0026] ;

[0027] in, Indicates the first Layer height observation data matrix Gram matrix, superscript Indicates matrix transpose;

[0028] Then, the eigenvalue matrix of the height Gram matrix for each layer is calculated using library functions. The expression is:

[0029] ;

[0030] in, Indicates the first The eigenvalue matrix of the layer height Gram matrix, This refers to library functions for calculating the eigenvalues ​​of a Gram matrix;

[0031] Then, the largest eigenvalue is selected from the eigenvalue matrix of each layer's height. And calculate the largest eigenvalue. The square root of the value yields the maximum singular value for each floor's height. The expression is:

[0032] ;

[0033] Finally, based on the maximum singular value of each floor's height... The feature weighting coefficients for each layer height were calculated separately. The expression is:

[0034] ;

[0035] in, Indicates the first The maximum singular value of the floor height, Indicates the first The maximum eigenvalue of the layer height, Indicates the first The characteristic weighting coefficient of the layer height.

[0036] Preferably, in step 6, the weight calculation module calculates the weight factor for each layer height. The calculation expression is:

[0037] ;

[0038] in, The balance coefficient represents the equilibrium between overall energy and the strongest characteristic. Indicates the first The weighting factor for floor height, Indicates the first Energy weighting factor based on floor height, Indicates the first The characteristic weighting coefficient of the layer height.

[0039] The weighting factor is used to balance the contributions of aeromagnetic anomalies at different altitudes in the inversion process, making them equal and preventing data from one altitude from playing a dominant role in the inversion.

[0040] Preferably, in step 7, the expression for the joint inversion objective equation is:

[0041] ;

[0042] ;

[0043] ;

[0044] in, Represents the total kernel matrix. Represents the total data matrix. Indicates the first The weighting factor for floor height, Indicates the first The kernel matrix at layer height, Indicates the first The observation data matrix of floor height, ; This represents the magnetization intensity distribution matrix, i.e., the ore body distribution state data; superscript This indicates the matrix transpose.

[0045] During joint inversion, aeromagnetic anomaly observation data at each level were used. and its corresponding kernel matrix Weighting is performed by multiplying the corresponding weight factors by the magnetic anomaly and the kernel matrix, and then forming the total data matrix. and total kernel matrix Finally, a joint inversion objective equation is constructed for inversion iteration.

[0046] The beneficial effects of this invention are: the method provided by this invention can directly generate weight factors corresponding to different heights based on the overall energy and strongest features of data at different heights, avoiding repeated trial and error in the weight selection process and greatly improving inversion efficiency. This method can be widely applied to the calculation of weight factors in various mining areas, demonstrating high applicability. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention;

[0048] Figure 2 This is a planar contour map showing the height of each layer in the embodiment. Detailed Implementation

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific examples. The following embodiments or drawings are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0050] like Figure 1 As shown, a method for calculating the weighting factor in the joint inversion of multi-altitude aeromagnetic anomalies includes the following steps:

[0051] Step 1: Construct a multi-altitude aeromagnetic anomaly joint inversion weight factor calculation system. This multi-altitude aeromagnetic anomaly joint inversion weight factor calculation system is equipped with a data acquisition module, an image rendering module, a weight calculation module, and an inversion calculation module connected in sequence.

[0052] Step 2: The data acquisition module collects magnetic anomaly data at a set height in n layers of the ore body area, as well as the coordinates corresponding to each magnetic anomaly data;

[0053] Step 3: The image rendering module acquires the magnetic anomaly data and its corresponding coordinates, then performs gridding based on the coordinates, and draws planar contour maps of the magnetic anomaly data at each layer height, such as... Figure 2 As shown; Figure 2 The planar contour maps corresponding to six altitude magnetic anomaly data points of 20m, 100m, 200m, 300m, 400m and 500m are displayed respectively.

[0054] Step 4: The weight calculation module selects observation data with the same XY coordinates (i.e., magnetic anomaly data) from the grid data of each layer based on the plane contour map of each layer, and constructs the observation data matrix for each layer. , ;

[0055] Step 5: The weight calculation module calculates the weights based on the observed data matrix. The observation data matrix corresponding to each layer height was calculated separately. Energy weighting coefficient at this height of overall energy and the feature weighting coefficient on the strongest feature at that height ;

[0056] Step 6: The weight calculation module calculates the weights based on the energy weighting coefficients. and feature weighting coefficients The weighting factors corresponding to the aeromagnetic anomalies at each altitude were calculated separately. ;

[0057] Step 7: The inversion calculation module calculates the weighting factors. A joint inversion objective equation for the distribution of ore bodies is constructed, and inversion iterative calculations are performed to obtain the ore body distribution status data of the ore body region.

[0058] The data acquisition module is a drone, which is equipped with a magnetic probe and a GPS probe.

[0059] In step 2, the UAV flies at different set altitudes. The magnetic probe on the UAV is used to collect magnetic anomaly data of the ore body area at the corresponding altitude, and the GPS probe on the UAV is used to collect the coordinates and altitude information of each observation point.

[0060] In step 5, the weight calculation module calculates the energy weighting coefficients for each layer height. The calculation expression is:

[0061] ;

[0062] ;

[0063] in, Indicates the first The root mean square of the layer height observation data matrix, ; This indicates the total number of observation data in the current layer. Indicates the current layer's first... One observation data, ; Indicates the first Energy weighting coefficient for floor height.

[0064] In step 5, the weight calculation module calculates the feature weighting coefficients for each layer height. The calculation process is as follows:

[0065] First, the Gram matrix of the observation data matrix for each layer is calculated, as shown in the following expression:

[0066] ;

[0067] in, Indicates the first Layer height observation data matrix Gram matrix, superscript Indicates matrix transpose;

[0068] Then, the eigenvalue matrix of the height Gram matrix for each layer is calculated using library functions. The expression is:

[0069] ;

[0070] in, Indicates the first The eigenvalue matrix of the layer height Gram matrix, This refers to library functions for calculating the eigenvalues ​​of a Gram matrix;

[0071] Then, the largest eigenvalue is selected from the eigenvalue matrix of each layer's height. And calculate the largest eigenvalue. The square root of the value yields the maximum singular value for each floor's height. The expression is:

[0072] ;

[0073] Finally, based on the maximum singular value of each floor's height... The feature weighting coefficients for each layer height were calculated separately. The expression is:

[0074] ;

[0075] in, Indicates the first The maximum singular value of the floor height, Indicates the first The maximum eigenvalue of the layer height, Indicates the first The characteristic weighting coefficient of the layer height.

[0076] In step 6, the weight calculation module calculates the weight factor for each layer height. The calculation expression is:

[0077] ;

[0078] in, The balance coefficient represents the equilibrium between overall energy and the strongest characteristic. Indicates the first The weighting factor for floor height, Indicates the first Energy weighting factor based on floor height, Indicates the first The characteristic weighting coefficient of the layer height.

[0079] In step 7, the expression for the joint inversion objective equation is:

[0080] ;

[0081] ;

[0082] ;

[0083] in, Represents the total kernel matrix. Represents the total data matrix. Indicates the first The weighting factor for floor height, Indicates the first The kernel matrix at layer height, Indicates the first The observation data matrix of floor height, ; superscript Indicates matrix transpose; This represents the magnetization intensity distribution matrix, i.e., the orebody distribution status data. This orebody distribution status data reflects the mineral distribution in the orebody area and can be used for mineral exploration.

[0084] This invention directly generates weight factors corresponding to different heights based on the overall energy and strongest features of data at different heights, avoiding repeated trial and error in the weight selection process and greatly improving inversion efficiency.

[0085] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the weighting factor in the joint inversion of multi-altitude aeromagnetic anomalies, characterized in that, Includes the following steps: Step 1: Construct a multi-altitude aeromagnetic anomaly joint inversion weight factor calculation system. This multi-altitude aeromagnetic anomaly joint inversion weight factor calculation system is equipped with a data acquisition module, an image rendering module, a weight calculation module, and an inversion calculation module connected in sequence. Step 2: The data acquisition module collects magnetic anomaly data at a set height in n layers of the ore body area, as well as the coordinates corresponding to each magnetic anomaly data; Step 3: The image rendering module acquires the magnetic anomaly data and its corresponding coordinates, then performs gridding based on the coordinates, and draws a planar contour map of the magnetic anomaly data at each layer height; Step 4: The weight calculation module selects observation data with the same XY coordinates (i.e., magnetic anomaly data) from the grid data of each layer based on the plane contour map of each layer, and constructs the observation data matrix for each layer. , ; Step 5: The weight calculation module calculates the weights based on the observed data matrix. The observation data matrix corresponding to each layer height was calculated respectively. Energy weighting coefficient at this height of overall energy and the feature weighting coefficient on the strongest feature at that height ; Step 6: The weight calculation module calculates the weights based on the energy weighting coefficients. and feature weighting coefficients The weighting factors corresponding to the aeromagnetic anomalies at each altitude were calculated separately. ; Step 7: The inversion calculation module calculates the weighting factors. A joint inversion objective equation for the distribution of ore bodies is constructed, and inversion iterative calculations are performed to obtain the ore body distribution status data of the ore body region.

2. The method for calculating the weighting factor of multi-altitude aeromagnetic anomaly joint inversion according to claim 1, characterized in that: The data acquisition module is a drone, which is equipped with a magnetic probe and a GPS probe. In step 2, the UAV flies at different set altitudes. The magnetic probe on the UAV is used to collect magnetic anomaly data of the ore body area at the corresponding altitude, and the GPS probe on the UAV is used to collect the coordinates and altitude information of each observation point.

3. The method for calculating the weighting factor of multi-altitude aeromagnetic anomaly joint inversion according to claim 1, characterized in that: In step 5, the weight calculation module calculates the energy weighting coefficients for each layer height. The calculation expression is: ； ； in, Indicates the first The root mean square of the layer height observation data matrix, ; This indicates the total number of observation data in the current layer. Indicates the current layer's first... One observation data, ; Indicates the first Energy weighting coefficient for floor height.

4. The method for calculating the weighting factor of multi-altitude aeromagnetic anomaly joint inversion according to claim 1, characterized in that: In step 5, the weight calculation module calculates the feature weighting coefficients for each layer height. The calculation process is as follows: First, the Gram matrix of the observation data matrix for each layer is calculated, as shown in the following expression: ； in, Indicates the first Layer height observation data matrix Gram matrix, superscript Indicates matrix transpose; Then, the eigenvalue matrix of the height Gram matrix for each layer is calculated using library functions. The expression is: ； in, Indicates the first The eigenvalue matrix of the layer height Gram matrix, This refers to library functions for calculating the eigenvalues ​​of a Gram matrix; Then, the largest eigenvalue is selected from the eigenvalue matrix of each layer's height. And calculate the largest eigenvalue. The square root of the value yields the maximum singular value for each floor's height. The expression is: ； Finally, based on the maximum singular value of each floor's height... The feature weighting coefficients for each layer height were calculated separately. The expression is: ； in, Indicates the first The maximum singular value of the floor height, Indicates the first The maximum eigenvalue of the layer height, Indicates the first The characteristic weighting coefficient of the layer height.

5. The method for calculating the weighting factor of multi-altitude aeromagnetic anomaly joint inversion according to claim 1, characterized in that: In step 6, the weight calculation module calculates the weight factor for each layer height. The calculation expression is: ； in, The balance coefficient represents the equilibrium between overall energy and the strongest characteristic. Indicates the first The weighting factor for floor height, Indicates the first Energy weighting factor based on floor height, Indicates the first The characteristic weighting coefficient of the layer height.

6. The method for calculating the weighting factor of multi-altitude aeromagnetic anomaly joint inversion according to claim 1, characterized in that: In step 7, the expression for the joint inversion objective equation is: ； ； ； in, Represents the total kernel matrix. Represents the total data matrix. Indicates the first The weighting factor for floor height, Indicates the first The kernel matrix at layer height, Indicates the first The observation data matrix of floor height, ; This represents the magnetization intensity distribution matrix, i.e., the ore body distribution state data; superscript This indicates the matrix transpose.

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