Combining Edge Enhancement Images for More Reliable Detection of Magnetic Features: A Python implementation

Abstract

Summary The most common use of aeromagnetic data is the identification of magnetic bodies and contacts. Edge enhancement techniques are crucial to the interpretation process because they allow more accurate mapping of these key features. However, most techniques used to enhance magnetic features have disadvantages of one type or another. The algorithm presented here allows the user to apply any combination of fourteen different enhancement filter techniques. This strategy has the advantage of letting the interpreter to compare the noise-to-signal ratio obtained for different methods and chose only the better results for a specific study case. We also included two different options to combine the results: a simple stacking approach where all filters considered have the same weight to compose the final map and one that divides the solutions in four different groups, according with the number of results obtained. By stacking the solutions obtained by different filters it is possible to enhance true edges while minimizing false peaks and mathematical artefacts. The method was tested on a synthetic data set and one real case to demonstrate the methods performance. The synthetic case was designed to simulate the presence of three sources at different depths with a strong unknown remanent component.