Airborne gravity gradient surveys yield comprehensive measurements of the Earth's gravity tensor, offering critical insights for geological structural analysis. These data facilitate the delineation of subsurface sources and the spatial extent of structures responsible for gravity anomalies through the integrated interpretation of each tensor component. Given the inherent gradient nature of the acquired data, their combined analysis serves as a robust tool for enhancing structural boundary detection. However, airborne gravity gradient measurements are often significantly contaminated by noise, necessitating the direct utilization of tensor components without further re-derivative to ensure reliable identification of buried targets. In this study, we employ a novel sigmoid derivative function applied to the normalized components of the directional analytical signal of the gradient tensor to improve the resolution of buried target boundaries.
Abedi,M . (2025). Sigmoid derivative mapping of full tensor potential field gravity data for edge enhancement. Journal of Algorithms and Computation, 57(1), 146-156. doi: 10.22059/jac.2025.398419.1236
MLA
Abedi,M . "Sigmoid derivative mapping of full tensor potential field gravity data for edge enhancement", Journal of Algorithms and Computation, 57, 1, 2025, 146-156. doi: 10.22059/jac.2025.398419.1236
HARVARD
Abedi M. (2025). 'Sigmoid derivative mapping of full tensor potential field gravity data for edge enhancement', Journal of Algorithms and Computation, 57(1), pp. 146-156. doi: 10.22059/jac.2025.398419.1236
CHICAGO
M Abedi, "Sigmoid derivative mapping of full tensor potential field gravity data for edge enhancement," Journal of Algorithms and Computation, 57 1 (2025): 146-156, doi: 10.22059/jac.2025.398419.1236
VANCOUVER
Abedi M. Sigmoid derivative mapping of full tensor potential field gravity data for edge enhancement. J. Algo. Comp.. 2025;57(1):146-156. doi: 10.22059/jac.2025.398419.1236
