Airborne electromagnetic data levelling based on the structured variational method

Abstract

Levelling errors are defined as the data difference among flight lines in airborne geophysical data. The differences in the signal levelling always appear as a striping pattern parallel to the flight lines on the imaged maps. The fixed structured pattern inspires us to structure a guided levelling error model using an anisotropic Gabor filter. We then embed the levelling error model into a total variational framework to flexibly calculate levelling errors. The guided levelling error model constrains the noise term of total variation rather than just using blind removal. Moreover, we can also apply the structured variational method to remove other noises in airborne geophysical data. This would just require replacing the noise prior models in the proposed method. We have applied this method to the airborne electromagnetic, magnetic, and apparent conductivity data collected by the Ontario Geological Survey to confirm its validity and robustness by comparing the results with the published data. The structured variational method can better level the airborne geophysical data based on the space properties of the levelling error.