Novel Detectors Based on the Elliott Function for Mapping Potential Field Data: Application to Aeromagnetic Data from Indiana, United States

Abstract

One of the primary objectives in interpreting potential field data is to delineate the horizontal boundaries of subsurface geological structures. Several detectors have been developed to achieve this goal by utilizing field-directional gradients. However, these detectors are associated with limitations, such as low resolution, the generation of spurious edges, and dependence on the depth of the causative source. In this study, we introduce two novel detectors that combine gradient amplitude derivatives with the Elliott function (EF) and a modified version of the Elliott function (MEF) to enhance the clarity and precision of boundary identification. The effectiveness of these proposed techniques is demonstrated through the evaluation of synthetic gravity and magnetic datasets, as well as a real case study from the Indiana region in the United States. To mitigate noise in both synthetic and real models, vertical derivatives have been calculated using the β-vertical derivative ratio (β-VDR) technique. Our results indicate that the proposed filters can reduce artifacts in the pseudo-boundary map and produce high-resolution outcomes.