Edge Detection of Potential Field Gradient Tensor Data with Horizontal Second Order Directional Analytic Signal

Abstract

Potential-field gradient tensor data include nine signal components. They include higher frequency signals than potential field data, which can help to delineate small-scale features of the sources. Edge detection technologies have been widely used to delineate the edges of the sources. We need to develop new edge detector to process the gradient tensor data. There are many methods are used to recognize the edges of the data. The analytic signal method is a widely used edge detection filter. We make some improvements to the analytic signal method, so that it can process the potential-field gradient tensor data. We define a few new filters based on the horizontal directional analytic signal and second order horizontal directional analytic signal. In order to display the large and small amplitude edges simultaneously, we present a normalization method. These methods have been tested on synthetic and real potential-field gradient tensor data to verify the feasibility. Compared to other known balance filters, the normalized second order horizontal directional analytic signal can get the results clearly and precisely.