A robust edge-detection approach for precise delineation of deep structures from potential field data: Structural insights supported by remote sensing

Abstract

Abstract This study presents a unique Logsigm Function (LSF) filter for edge detection, established to improve the delineation of geological structures from potential field data. The filter’s performance was first validated using synthetic gravity and magnetic models simulating complex geological configurations with varying depths and contrasts. Results confirm the LSF’s high precision in resolving both shallow and deep structural boundaries, even in the presence of noise, while maintaining computational simplicity and ease of implementation. The method was then applied to real gravity and magnetic data from Northern Sinai, Egypt, a geologically complex region affected by extensional and inversion tectonics. To complement and validate the geophysical results, surface lineaments were extracted from enhanced remote sensing datasets, including Landsat8 OLI and ALOS PALSAR DEM imagery. The comparison between surface and subsurface trends revealed systematic vertical variations in structural orientations, highlighting the role of inherited basement faults and deformation decoupling. The LSF results successfully matched known structures and uncovered previously unrecognized lineaments, offering new insights into the tectonic architecture and basin evolution of Northern Sinai. The integrated approach demonstrates the value of combining advanced filtering techniques with remote sensing to achieve robust structural interpretations. The simplicity, stability, and high resolution of the LSF method make it a powerful tool for structural geology, tectonic analysis, and resource exploration in complex geologic terrains.