Normalized facet edge detection and enhancement in potential field sources with the scale-space technique

Edge detection and enhancement is an important technique in interpretation of potential field data. Many existing edge detection and enhancement methods are usually unable to enhance the signals of the shallow and deep geological sources. In this study, we present an edge detection method to enhance the weak and noisy signals produced by shallow and deep mass anomalies. This new technique is based on a combination of the normalized cubic facet model and the scale-space technique which uses a band-pass filter taking the differences between two Poisson scale-space representations of the potential-field data. Compared to the existing edge detection algorithms, this new algorithm can more accurately recover the edges and shapes of the underground anomalous bodies with numerical stability. It can also effectively suppress noise as the high-order derivatives are used due to the Poisson scale-space representation which is equivalent to performing an upward continuation of data. Tests on synthetic gravity data and real gravity anomaly data collected from the Witwatersrand Basin in South African and the real aeromagnetic anomaly data from Tongling of China are performed to verity this new edge detection and enhancement algorithm. Results show that different applications of newly developed filter are feasible to interpret other geologic environments with similar challenges of enhancing the field responses of deep and shallow sources. Comparisons with the HGA, tilt angle, NSTD and theta map indicate that this new filter yields superior results and produces better interpretive images than the other filters.