Abstract

Determining the horizontal position of magnetic anomalies is essential for interpreting subsurface geological structures in geophysical exploration. Although numerous edge-detection techniques exist for outlining anomaly boundaries, many still struggle with issues such as false edge generation, poor resolution, and high noise sensitivity. This study introduces a new high-resolution approach that combines the Skewed-Sigmoid (Skewed-Sig) function with the Total Horizontal Gradient (THG) to overcome these limitations. The Skewed-Sig function resembles the commonly used arctangent operator found in many edge-detection filters, while the THG is a standard technique that often lacks sufficient edge-resolution when used alone. By integrating these two components, we develop a hybrid filter—referred to as SSF—that enhances the detection of mineralized targets. To evaluate the performance of the proposed SSF filter, we compare it with widely used methods, including the analytical signal (AS), THG, tilt angle (TA), theta map (TM), and total horizontal gradient of the tilt angle (TAHG), using a synthetic magnetic model with a complex subsurface structure. To create synthetic conditions that were close to the real world, noise was added to the model data, and the quality of the output maps was checked. The filter was then applied to magnetic data from the Shavaz iron deposit in Yazd Province, Iran. The study area lies within the Central Iran Block, a region that is known for its significant potential for iron mineralization, particularly hematite and magnetite. Our results demonstrate that SSF delineates the mine location with greater accuracy than traditional methods and shows strong agreement with existing geological information and previous investigations in the Shavaz region. Consequently, the SSF technique offers a reliable tool for detecting mineral deposits and other subsurface magnetic sources.