A Gridding Method for Geomagnetic Anomaly Data Based on Magnetic Dipole Equivalent Source Method

Abstract

The invention belongs to the field of geomagnetic abnormal data processing, and particularly relates to a geomagnetic abnormal data meshing method based on a magnetic dipole equivalent source method. The geomagnetic abnormal data meshing method includes determining an effective area, capable of generating a magnetic field, of each magnetic dipole, arranging the magnetic dipoles on a magnetic source plane, describing magnetic field observed values by the aid of the arranged magnetic dipoles, solving magnetic moments of the magnetic dipoles and solving magnetic field values of target grid points on an observed plane by the aid of the magnetic dipoles. Since the effective areas of the magnetic dipoles are set on the magnetic field observed plane, the complexity in processing of mass data is reduced effectively, and reduction of operation precision due to data partitioning in conventional equivalent source methods is avoided.

Description

technical field [0001] The invention belongs to the field of geomagnetic anomaly data processing, and in particular relates to a method for gridding geomagnetic anomaly data based on a magnetic dipole equivalent source method. Background technique [0002] Geomagnetic anomaly data are generated by rocks, minerals, and other magnetic features in the shallow crust and surface. They have rich features and strong stability. They are widely used in mineral exploration, geological research, and vehicle navigation. The distribution is uneven. In order to facilitate data analysis and engineering applications, it is first necessary to grid the measurement data. [0003] At present, there are many methods for gridding spatially scattered data, which can be divided into three categories. One is to use the interpolation method to estimate the position of the grid point. The known point is used as a reference point to estimate the position. Representative methods include kriging method,...

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Problems solved by technology

[0003] At present, there are many methods for gridding spatially scattered data, which can be divided into three categories. One is to use the interpolation method to estimate the position of the grid point. The known point is used as a reference point to estimate the position. Representative methods include kriging method, radial basis function method, inverse distance weighting method, spline function method, Shepherd method, etc.; the second is the model method, that is, using all known Establish a unified mathematical model for the research area based on the known data, and use the established model to solve the magnetic field value at the grid point position. Representative methods include Taylor polynomial method, moment harmonic model method, spherical harmonic or crown harmonic model method, However, the disadvantage of this type of method is that it is difficult to accurately describe the magnetic field value of the entire area with a unified expression, especially for the geomagnetic anomaly field that changes drastically with the location. When the amount is large, the effectiveness of this method will be greatly weakened; the third is to use the intelligent prediction algorithm, that is, to use the known data as the training set to train the intelligent algorithm model, and then use the trained model to predict the magnetic field value of the grid point position Forecasting, the advantage of this type of method is that the data processing effect on the boundary area is relatively stable, but the overall prediction accuracy is relatively limited, and it depends heavily on the quantity and quality of training data

[0004] In addition, all the above methods have a common feature, they are all based on the mathematical operation process of the measurement data itself, and ignore the analysis from the principle of magnetic field generation

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