High-precision vector
magnetic field detection is widely applied to the fields of
celestial body magnetic field detection, aeromagnetic detection, ocean
magnetic field detection, geomagnetic navigation and the like at present and is installed on airborne magnetic detection platforms such as satellites, airplanes, unmanned aerial vehicles, submarines and the like, and due to the fact that the
data acquisition amount is large, calculation-intensive processes such as
processing of a large amount of magnetic vector data and three-dimensional inversion are involved. The invention provides a novel inversion method for directly extracting parameters from a magnetic
tensor gradient data image by using a CNN (
Convolutional Neural Network) and synthesizing the parameters to generate a model matched with a detection target body. The method comprises the following steps of: performing forward modeling on a synthetic source body to obtain enough magnetic
tensor gradient data samples; and adjusting a volume CNN structure, adding a shear layer, and realizing prediction of each parameter. Through single and double cube model numerical
simulation, the
algorithm accuracy is verified. According to numerical
simulation and comparison test results, the CNN network has good
nonlinear inversion capability, can realize accurate and rapid prediction of magnetic
tensor gradient inversion, and is high in practicability and wide in application range.