Comparison of derivative-based methods by normalized standard deviation approach for edge detection of gravity anomalies


This paper describes the application of the so-called normalized standard deviation (NSTD) method to detect edges of gravity anomalies. Using derivative-based methods enhances the anomaly edges, leading to significant improvement of the interpretation of the geological features. There are many methods for enhancing the edges, most of which are high-pass filters based on the horizontal or vertical derivatives of gravity data. The normalized standard deviation, a new edge detection filter, is based on the moveable windows through gradient data, i.e. gravity gradient. The NSTD method (as an equation of the ratio of the related normalized standard deviations of the gravity data gradients) along with comparable techniques, including analytic signal, total horizontal derivative (THD), tilt angle, total horizontal derivative of tilt angle (THDT) and theta map, are examined for noise-free and noise-added synthetic data. The aim is to demonstrate the suitability of the NSTD in edge enhancement. Having obtained satisfactory results, the methods are applied successfully to the real gravity data of Dehloran Bitumen and the Karst zones in SabzKoh. The main aim of the edge detection methods in our study is to determine the appropriate locations of exploratory drillings in gravity prospect. It is demonstrated that suitable locations are determined based on these methods.