Mitigation Methods of Short-time Diurnal Magnetic Noise in Airborne Magnetic Survey

Abstract

The short-time diurnal variation magnetic noise is an important factor restricting the further improvement of the quality of airborne magnetic survey data. Since it is random and nonstationary, simple filtering methods cannot suppress it. Considering its characteristics of wide spatial distribution and strong spatial consistency, this paper proposes two adaptive filtering methods, Kalman filtering method and an improved normalized Least Mean Square (INLMS) adaptive filtering algorithm, to remove the diurnal variation magnetic noise measured by airborne magnetic sensors. These two methods can not only reduce in real time the influence of short-time diurnal variation magnetic noise by setting a reference magnetic sensor, but also avoid the influence of the outliers in the reference magnetic data. The actual experiments show that the two methods can effectively suppress the diurnal variation magnetic noise in airborne magnetic survey data, thereby improving the signal-noise ratio (SNR) of magnetic survey data and recovering the target magnetic signal well.