A 3D resistivity mapping technique has been developed to provide fast estimates of resistivity distributions in airborne electromagnetic surveys. This proposed 3D mapping method consists of an approximate 3D linear inverse operator and a generalized subspace solver. The 3D inverse operator can be generated using any forward approximation that is linear in resistivity. The generalized subspace method is an alternative to the conjugate gradient method, and it reduces the original large linear system of equations to a much smaller but nonlinear one that is solved iteratively. The major benefit of using generalized subspace methods is that subspace vectors can be built based upon physical principles such as skin and investigation depths. Since the 3D mapping is a linear inverse problem, no iteration, and thus no forward modeling nor sensitivity updating, is needed. The 3D resistivity‐mapping technique can be used directly to estimate 3D resistivity distribution or to provide a model update during an intermediate iteration in a nonlinear 3D inversion. Synthetic and field data examples indicate that the 3D mapping can provide quantitative information about the resistivity and spatial distributions of the 3D targets.
Semantic 3D Mapping from Deep Image Segmentation
