Accuracy Evaluation of Calibrating Magnetic Tensor Gradiometers From Total-Field Gradient Measurements in Aeromagnetic Survey

The magnetic gradient tensor is the spatial rate of change of the magnetic vector along the three axes of the Cartesian coordinate system. It is particularly suitable for aeromagnetic surveys because it is relatively insensitive to temporal changes and regional variations of the geomagnetic field. However, the complexity of the flight environment and defects in the existing calibration methods for airborne magnetic tensor gradiometers cannot guarantee the measurement accuracy of tensor data. Research shows that the generalized Hilbert transform can convert total-field gradients, measured over an extensive survey area, into the magnetic gradient tensor components, so a high-precision total-field gradiometer can be employed to calibrate a magnetic tensor gradiometer, using a generalized Hilbert transform calibration method (GHTCM). However, practical survey conditions cannot meet the requirements of the generalized Hilbert transform, which degrades the accuracy of GHTCM. Based on the theory of the generalized Hilbert transform, we first analyze the error sources and error mechanisms of the GHTCM according to the differences between real and ideal survey conditions and then build models to design an appropriate evaluation algorithm for the accuracy of the GHTCM. Simulation experiments based on the open-source flight simulator, FlightGear, and a practical flight experiment are designed. The results indicate that the proposed method can correctly evaluate the accuracy of the GHTCM. This evaluation algorithm is the basis of obtaining the high-precision calibration data for practical airborne magnetic gradient tensor in the survey.

Automated summary

The magnetic gradient tensor represents the spatial rate of change of the magnetic vector along the three axes of the Cartesian coordinate system and is suitable for aeromagnetic surveys. However, existing calibration methods are flawed, necessitating the development of improved evaluation algorithms to enhance accuracy.