A combination of the logistic function and the directional Hilbert transform for edge detection of gravity data

Articles in Press

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Tehran university

Abstract

Gravity anomalies map feature spatially overlapping fingerprints from a potentially huge number of sources, each with a different shape, depths and densities. In recent decades, geophysicists have developed a variety of image-enhancement filtering algorithms or edge detectors to accurately display the geometry and details of subterranean sources. However, in common filters, the images obtained for deeper sources are usually diffuse or fuzzy, or filters cannot equalize the edges of weak and strong amplitudes at the same time. In order to overcome these problems, in this paper, an effective filter using the combination of the logistic function, the directional Hilbert transform and the total horizontal gradient is introduced, abbreviated as "MLK". The results on the 2D and 3D data with noise and without noise show that MLK proposed filter performs better compared to the other filters. Also, we used the measured complete Bouguer anomaly (CBA) high resolution dataset in Slovakia territory. In addition to gravity map we also used the geological map to control our results as a case study. The primary goal of this research is to compare MLK edge determination filter and to assess the quality of filter and balance signals from shallow and deep geological structures.

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Articles in Press, Accepted Manuscript
Available Online from 22 August 2026

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