A dual estimate method for aeromagnetic compensation

摘要

Scalar aeromagnetic surveys have played a vital role in prospecting. However, before analysis of the surveys' aeromagnetic data is possible, the aircraft's magnetic interference should be removed. The extensively adopted linear model for aeromagnetic compensation is computationally efficient but faces an underfitting problem. On the other hand, the neural model proposed by Williams is more powerful at fitting but always suffers from an overfitting problem. This paper starts off with an analysis of these two models and then proposes a dual estimate method to combine them together to improve accuracy. This method is based on an unscented Kalman filter, but a gradient descent method is implemented over the iteration so that the parameters of the linear model are adjustable during flight. The noise caused by the neural model's overfitting problem is suppressed by introducing an observation noise.