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Abstract

Drone-based measurements facilitate geophysical data acquisition to obtain extensive surveys at high spatial resolutions, especially among inaccessible areas such as forests and lakes. The DroneSOM (Drone Geophysics and Self-Organizing Maps) project, funded by EIT RawMaterials, intends to develop drone-based gravity and electromagnetic (EM) exploration instruments and associated data interpretation software, including an efficient and robust 3D EM inversion code. We hereby present the framework and show some preliminary results of the development through numerical experiments.<p>The modeling was addressed by solving Maxwells’ equations using a total field formulation. The code supports rectilinear and octree gridding. The edge finite element method was used for the equation discretization. To solve the resulting linear system of equations, we use a direct solver (MUMPS). The code is implemented in C++ and allows for easy adaptation for various sources and data types. To solve the inverse problem, we minimize the misfit using a Gauss-Newton scheme with explicit computation of the Jacobian. In each iteration, we solve for the search direction by iterative Krylov solvers such as conjugated gradients (PETSc).<p>The implementation was built on deal.II library, where the interface wrappers to MUMPS and PETSc facilitate performing heavy computation, such as system equation solving and inversion model update. Currently, the code is parallelized using OpenMP for MUMPS and MPI throughout both forward and inverse modeling. The code is designed to be a reliable and competent imaging tool, that can be applied for both commercial and educational use.