Edge Detection in Potential-Field Data by Enhanced Mathematical Morphology Filter

Abstract

Enhancement on the edges of the causative source is an indispensable tool in the interpretation of potential-field data. There are a number of methods for recognizing the edges, most of which involve high-pass filters based on derivatives of potential-field data. A new edge-detection method is presented, called the enhanced mathematical morphology (EMM) filter. The EMM filter uses the ratio of the erosion of the total horizontal derivative to the dilation of total horizontal derivative to recognize the edges of the sources, and can display the edges of the shallow and deep bodies simultaneously. The EMM filter does not require the computation of vertical derivatives, which makes this method computationally stable. The EMM filter is tested on synthetic and real potential field data in China. Compared to other edge-detection filters, the new method is able to recognize the source edges more clearly, and the outputs are more insensitive to noise.