Frequency-domain airborne electromagnetics is a proven geophysical exploration method.Presently,the interpretation is mainly based on resistivity-depth imaging and onedimensional layered inversion;nevertheless,it is difficult to obtain satisfactory results for two- or three-dimensional complex earth structures using 1D methods.3D forward modeling and inversion can be used but are hampered by computational limitations because of the large number of data.Thus,we developed a 2.5D frequency-domain airborne electromagnetic forward modeling and inversion algorithm.To eliminate the source singularities in the numerical simulations,we split the fields into primary and secondary fields.The primary fields are calculated using homogeneous or layered models with analytical solutions,and the secondary（scattered） fields are solved by the finite-element method.The linear system of equations is solved by using the large-scale sparse matrix parallel direct solver,which greatly improves the computational efficiency.The inversion algorithm was based on damping leastsquares and singular value decomposition and combined the pseudo forward modeling and reciprocity principle to compute the Jacobian matrix.Synthetic and field data were used to test the effectiveness of the proposed method.
The travel time and amplitude of ground-penetrating radar （GPR） waves are closely related to medium parameters such as water content, porosity, and dielectric permittivity. However, conventional estimation methods, which are mostly based on wave velocity, are not suitable for real complex media because of limited resolution. Impedance inversion uses the reflection coefficient of radar waves to directly calculate GPR impedance and other parameters of subsurface media. We construct a 3D multiscale stochastic medium model and use the mixed Gaussian and exponential autocorrelation function to describe the distribution of parameters in real subsurface media. We introduce an elliptical Gaussian function to describe local random anomalies. The tapering function is also introduced to reduce calculation errors caused by the numerical simulation of discrete grids. We derive the impedance inversion workflow and test the calculation precision in complex media. Finally, we use impedance inversion to process GPR field data in a polluted site in Mongolia. The inversion results were constrained using borehole data and validated by resistivity data.
