A Ground-Airborne Frequency-Domain Electromagnetic Rapid Imaging Method Based on Scalar Magnetic Field for Eliminating the Influence of Flight Attitude

The ground-airborne frequency-domain electromagnetic (GAFEM) method has the potential to detect underground anomalies at large depth ranges in areas with complex terrain. However, its specific configuration, which involves ground-based transmitting and airborne signal acquisition, inevitably introduces attitude noise into the measured magnetic field vector data. The presence of this attitude noise alters the characteristics of the measured data, consequently compromising the accuracy of imaging methods that rely on vector data. To eliminate the influence of attitude on GAFEM, this study proposes a rapid imaging technique for GAFEM based on the scalar magnetic field. This method utilizes the total magnetic field magnitude at each measurement point along the survey line as the input for imaging parameter calculation, thereby circumventing the effects of attitude noise and enabling high-resolution detection of underground anomalies. This study begins by analyzing the mechanism through which flight attitude affects GAFEM. Using a previously published GAFEM imaging method based on vector magnetic fields, it is demonstrated that flight attitude severely degrades the accuracy of such methods. Furthermore, a new imaging approach is proposed that employs the scalar magnetic field, which is immune to variations in flight attitude. A detailed description of the method’s principles, physical basis, and computational procedures is provided. The feasibility of the method is validated using a synthetic GAFEM model. The results indicate that the proposed method can achieve high-resolution detection of underground anomalies without being affected by attitude noise. Finally, the performance of the method is further tested using a simulation model constructed from actual geological data. The results confirm that the proposed method possesses the capability to identify underground anomalies in a complex model.