A Computationally Efficient Tool for Assessing the Depth Resolution in Potential-Field Inversion

In potential-field inversion problems, it can be difficult to obtain reliable information about the source distribution with respect to depth. Moreover, spatial resolution of the solution decreases with depth, and in fact the more ill-posed the problem – and the more noisy the data – the less reliable the depth information. Based on early work in depth resolution, defined in terms of the singular value decomposition, we introduce a tool APPROXDRP which uses an approximation of the singular vectors obtained by the iterative Lanczos bidiagonalization algorithm, making it well suited for large-scale problems. This tool allows a computational/visual analysis of how much the depth resolution in a computational potential-field inversion problem can be obtained from the given data.We show that when used in combination with a plot of the approximate SVD quantities, APPROXDRP may successfully show the limitations of depth resolution resulting from noise in the data. This allows a reliable analysis of the retrievable depth information and effectively guides the user in choosing the optimal number of iterations, for a given problem.