Abstract
Using observational data to determine the edges of the sources is an important task in the interpretation of potential field data. Extracting the edges of deep and shallow bodies effectively is the key to correctly understanding the underground structure. Based on the good multi-scale decomposition ability of two-dimensional variational mode decomposition (2D-VMD) and the outstanding shape analysis capability of mathematical morphology (MM), a new multi-scale edge detection method for potential field data is proposed. We propose using the variance of this morphological filter as a basis for selecting the optimal structural element (SE) scale. By establishing theoretical models and comparing the results of our method with those of traditional edge detection methods, the proposed method is shown to be effective at detecting edges within potential field data. Taking the Hanmiao area of Chifeng city, Inner Mongolia, China, as an example, 1:50000 aeromagnetic data are processed and analysed by this method. The physical properties of the rocks in the study area are also discussed. The results of theoretical calculations and real data processing show that this method can accurately extract the edges of the sources at different scales. And the real data processing results show that this method is suitable for the identification of structural faults.
Highlights
The geological process is long and complex
We propose an edge detection method based on mathematical morphology (MM) by improving E1(x, y) and E2(x, y)
In this paper, 2D-variational mode decomposition (VMD) is applied to the multi-scale decomposition of potential field data for the first time
Summary
The geological process is long and complex. potential field data are composed of superimposed sources having different depths, shapes, sizes, densities, and magnetism. Edge detection effects of different methods for gravity anomaly of the second synthetic model: IMF2 and IMF1 obtained by the (a) and (b) 2D-VMD and MEE; IMF2 and IMF1 obtained by (c) and (d) 2D-VMD and Tilt;(e) to (f) small-scale and large-scale components obtained by the BEMD and Theta map methods (the black boxes are the locations of the models; The blue lines indicate the detected edge positions). Comparing the edge detection results before and after the addition of Gaussian noise, the difference between them is small This shows that the method of MEE based on SEs scale optimization to improve the edge accuracy has a strong anti-noise ability. IMF3 is a small-scale component, reflecting the anomaly characteristics of the magnetic anomalies with an equivalent source layer depth of 710 m; this anomaly is caused mainly by shallow geological bodies and local fractures
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