MTAHG and MTBHG: Modified Approaches for Interpreting Gravity Data

Abstract Gravity anomaly maps often contain spatially overlapping signatures from numerous sources, each with varying shapes, depths, and density contrasts. Locating these signatures using edge detection techniques is crucial for geological structural interpretation and imaging of horizontal boundar...

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Bibliographic Details
Main Authors: Hazel Deniz Toktay, Hanbing Ai, Ahmad Alvandi, Kejia Su, Jinlei Li
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2025-04-01
Series:Earth and Space Science
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Online Access:https://doi.org/10.1029/2024EA003900
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Summary:Abstract Gravity anomaly maps often contain spatially overlapping signatures from numerous sources, each with varying shapes, depths, and density contrasts. Locating these signatures using edge detection techniques is crucial for geological structural interpretation and imaging of horizontal boundaries. This paper proposes two effective edge detection tools: one combining the balanced total horizontal gradient (BHG), and the hyperbolic tangent function, abbreviated as “MTBHG”; and the other combining the tilt angle of the total horizontal gradient (TAHG) and the hyperbolic tangent function, abbreviated as “MTAHG.” Additionally, the Modified Non‐Local Means (MNLM) filter was applied to suppress possible noise effects amplified by the gradient calculation process. Synthetic tests validated that the MTAHG and MTBHG detectors outperform other representative detectors. Two high‐resolution gravity data sets from the Western Carpathians in Slovakia and the Witwatersrand Basin in South Africa were used to test the applicability of the modified methods. Results show that the modified detectors achieve superior edge delineation and avoid creating spurious anomalies or artifacts even in the presence of unwanted noise interference. Furthermore, by eliminating false tilt‐depth (TD) solutions via the edge detection results, we enhance the accuracy of depth estimates and facilitate the credible identification of both horizontal and vertical structure distributions.
ISSN:2333-5084