Enhtheta: A Novel balanced filter for Edge Detection of Potential Field anomalies

The magnetic and gravity data are potential field data. The magnetic method is based on variations in the magnetic field derived from lateral differences in the magnetization of the subsurface. Magnetic interpretation, as in gravity interpretation, operates at several levels of complexity. It can range from simple identification and location of anomalous magnetic bodies in the subsurface (Edge detection approaches) to three dimensional modeling leading to complete characterization of anomaly. The most commonly used edge detection filters for enhancing potential field data are vertical or horizontal derivatives. Once can combine these derivative filters and produce a new edge detection filters (Analytic signal and total horizontal derivative for example). The most commonly used edge detection filters are designed based on the horizontal and vertical derivatives of the data. In recent years a number of methods have been introduced for extracting edges of the potential field source such as: analytic signal amplitude (ASA), total horizontal derivative (THDR), tilt angle (TA), theta map, tilt angle of horizontal gradient (TAHG), balanced analytic signal (ASB), the tilt angle of the analytic signal amplitude (TASA), and analytic signal of the tilt angle (ASTA) (Miller and Singh 1994; Verduzco et al. 2004; Wijns et al. 2005; Cooper and Cowan 2006; Cooper, 2009; Cooper, 2014; Ferreira et al. 2013; Anasri and Alamdar, 2011). In other hand, the balanced derivatives filters (or local phase filters) are used to simultaneously emphases potential filed signals from sources at different depths. In this paper, I presented an improved balanced filter, Enhanced Theta filter – “Enhtheta”, which replaces the conventional THDR and ASA with balanced THDR and ASA in Theta filter equation. In particular, the presence of overlain shallow and deep magnetic/gravity sources leads to create strong and weak anomalies. Thus, if the observed data contains anomalies with large variation of amplitude, then the anomalies which might be geologically important but with small amplitudes may be hard to recognized. In such a dataset, the closely-spaced sources are difficult to delineate due to superposition effect. This new filtering technique makes a balancing between the strong and weak anomalies in original image, so produces a balanced theta map. The maximum amount of this filter delineate the anomalies edge. Moreover, its total horizontal derivative (THDR_Enhthet) can be used as an edge detector filter. The maximum value of the THDR_Enhtheta filter shows the edges of the anomalies. I revealed the capability of the proposed algorithm through both noise-free and noise–contaminated synthetic magnetic data from prismatic models and also on real aeromagnetic data from Bushveld complex, South Africa. The results of the new filter are compared with other edge detection filters, namely TDR, Theta and TDX. Enhtheta and its total horizontal derivative provide more accurate detection of the source edges in comparison with the other filtering techniques. Therefore, interpretation of the potential field data is more facilitate using the Enhtheta filtering method.