Abstract
Edge enhancement is a frequently used transformation of potential field data. Its goal is to sharpen the position of the subsurface structures. Here we propose a new method to enhance the edges of the sources causing the potential anomalies called normalized Harris filter (NHF), which is based on the Harris filter and amplitude balance. Three synthetic data sets are used to evaluate the performance of the proposed approach. The presented approach provides a better estimation of the sources’ edges when compared to the other methods. The proposed method is robust to noisy data and can avoid the generation of artificial edges, thereby reducing the ambiguity of interpretation. The testing on real data set from the Yili basin in Northwestern China demonstrates that the new approach highlights several anomalies not shown in the geological map or other methods. The proposed approach also shows the advantages of gradually enhancing the edges of the deep-seated structure. The results demonstrate that the proposed approach may be a better detector in qualitatively determining the edges of sources causing potential field data.
Highlights
Academic Editors: Guoqiang Xue, Potential field data contains all the signals from geological bodies with different locations and geometries and has a volume effect
The normalized Harris filter (NHF) response is the ratio of R to the upper envelope surface, and its maxima represent the source edges
The applicability of NHF to potential field data is shown to enhance the edges of the subsurface structures
Summary
Academic Editors: Guoqiang Xue, Potential field data contains all the signals from geological bodies with different locations and geometries and has a volume effect. Many data processing techniques have been developed to facilitate the interpretation. Where f denotes the potential field data. [12] employed sun shading to extract the features from potential field data. [14] used the 2D Gabor filter on the magnetic data to interpret the subsurface lineaments. [17] employed phase congruency to detect lineaments embedded in magnetic data. Provides strong invariance to rotation, scaling, and noise. It has been widely used in many scientific areas based on image processing techniques, such as camera calibration [20], image stitching [21], and unstructured finite-element meshing [22]. We employ the Harris filter to detect the edges of the subsurface structures from potential field data. We apply the NHF to real gravity data from the Yili basin
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
