Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA

Abstract

Residual magnetic error remains after standard levelling process. The weak non-geological effect, manifesting itself as streaky noise along flight lines, creates a challenge for airborne geophysical data processing and interpretation. Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step. In this paper, we propose a two-step procedure for single aerogeophysical data microleveling: a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures; second, the mixture of Gaussian robust principal component analysis (MoG-RPCA) is then used to separate the weak energy fine structures from the residual. The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA. The deep convolutional network does not need dataset for training and the handcrafted network serves as prior (deep image prior) to capture the low-level nature geological structures in the areogeophysical data. Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.