Robust Directional Analysis of Magnetic Microscopy Images Using Non‐Linear Inversion and Iterative Euler Deconvolution

In paleomagnetism, bulk measurements of entire samples have traditionally been used to characterize remanent magnetization. While effective, this approach provides only a general directional estimate and cannot resolve spatial variability or magnetization at the grain scale. Recent advances in magnetic microscopy (MM), due to its high spatial resolution and magnetic moment sensitivity, now allow imaging at the scale of individual mineral grains. In this study, we aim to obtain reliable paleomagnetic directions using only MM data. To achieve this, we apply Euler deconvolution to solve the linear problem and mitigate the non-uniqueness associated with inversion. As an additional step, we refine the recovered parameters using a nonlinear inversion and remove interfering signals between sources to minimize noise. This algorithm was applied to both synthetic and real data and compared to its predecessor. The results from synthetic data demonstrate that this new approach is able to detect weaker sources and produce more accurate grain-level results, which in turn leads to larger data sets and improved statistical characterization of the sample. For real data, we observe that the iterative method was significantly more efficient than its predecessor, successfully retrieving the natural remanent magnetization direction of a basaltic sample with 3° from the bulk measurement. This represents a significant step forward in applying MM data to paleomagnetic studies.