Normalized vertical derivatives in the edge enhancement of maximum-edge-recognition methods in potential fields

Abstract

Gravity and magnetic data have unique advantages for studying the lateral extents of geologic bodies. There is a class of methods for edge recognition called maximum-edge-recognition methods (MERMs) that use their extreme values to locate the edges of geologic bodies. These methods include the total horizontal derivative (THDR), the analytic signal amplitude, the theta map, and the normalized standard deviation. These are all first-order derivative-based techniques. There are also higher-order derivative-based methods that are derived from the first-order filters, for example, the THDR of the tilt angle. We have developed an edge-recognition filter that is based on the idea of the normalized vertical derivatives (VDRs) of existing methods. For each MERM, we first calculate its nth-order VDR and then use thresholding to locate its peaks. The peak values are subsequently normalized by the values of the original MERM. Testing on synthetic and real data indicates that the normalized VDRs of the MERMs have higher accuracy and better lateral resolution and they are more interpretable than existing techniques; thus, they are a worthwhile addition to the set of edge-detection tools for potential-field data.