Electrically anisotropic strata are abundant in nature, so their study can help our data interpretation and our understanding of the processes of geodynamics. However, current data processing generally assumes isotropic conditions when surveying anisotropic structures, which may cause discrepancies between reality and electromagnetic data interpretation. Moreover, the anisotropic interpretation of the time-domain airborne electromagnetic （TDAEM） method is still confined to one dimensional （1D） cases, and the corresponding three-dimensional （3D） numerical simulations are still in development. In this study, we expanded the 3D TDAEM modeling of arbitrarily anisotropic media. First, through coordinate rotation of isotropic conductivity, we obtained the conductivity tensor of an arbitrary anisotropic rock. Next, we incorporated this into Maxwell＇s equations, using a regular hexahedral grid of vector finite elements to subdivide the solution area. A direct solver software package provided the solution for the sparse linear equations that resulted. Analytical solutions were used to verify the accuracy and feasibility of the algorithm. The proven model was then applied to analyze the effects of arbitrary anisotropy in 3D TDAEM via the distribution of responses and amplitude changes, which revealed that different anisotropy situations strongly affected the responses of TDAEM.
To improve the inversion accuracy of time-domain airborne electromagnetic data, we propose a parallel 3D inversion algorithm for airborne EM data based on the direct Gauss-Newton optimization. Forward modeling is performed in the frequency domain based on the scattered secondary electrical field. Then, the inverse Fourier transform and convolution of the transmitting waveform are used to calculate the EM responses and the sensitivity matrix in the time domain for arbitrary transmitting waves. To optimize the computational time and memory requirements, we use the EM ＂footprint＂ concept to reduce the model size and obtain the sparse sensitivity matrix. To improve the 3D inversion, we use the OpenMP library and parallel computing. We test the proposed 3D parallel inversion code using two synthetic datasets and a field dataset. The time-domain airborne EM inversion results suggest that the proposed algorithm is effective, efficient, and practical.
