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.