Abstract
Aeromagnetic surveys play an important role in geophysical exploration and many other fields. In many applications, magnetometers are installed aboard an aircraft to survey large areas. Due to its composition, an aircraft has its own magnetic field, which degrades the reliability of the measurements, and thus a technique (named aeromagnetic compensation) that reduces the magnetic interference field effect is required. Commonly, based on the Tolles–Lawson model, this issue is solved as a linear regression problem. However, multicollinearity, which refers to the case when more than two model variables are highly linearly related, creates accuracy problems when estimating the model coefficients. The analysis in this study indicates that the variables that cause multicollinearity are related to the flight heading. To take this point into account, a multimodel compensation method is proposed. By selecting the variables that contribute less to the multicollinearity, different sub-models are built to describe the magnetic interference of the aircraft when flying in different orientations. This method restricts the impact of multicollinearity and improves the reliability of the measurements. Compared with the existing methods, the proposed method reduces the interference field more effectively, which is verified by a set of airborne tests.
Highlights
Aeromagnetic surveys originated in the 1930s for military applications [1] and play a very important role in many other fields, such as geophysical exploration
Differing from the present methods that regard the FOM flight as a whole, here we propose a multimodel method to compensate for the magnetic interference field of the aircraft, according to the flight heading
Magnetometers are usually equipped aboard an aircraft in many applications, such as geophysical exploration
Summary
Aeromagnetic surveys originated in the 1930s for military applications [1] and play a very important role in many other fields, such as geophysical exploration. Aeromagnetic compensation is mainly based on the Tolles–Lawson (T–L) model [5,6], which decomposes the magnetic interference field into three sources: the permanent field, induced field, and eddy-current field Combining all of these fields, the magnetic interference field can be described as a linear equation with 18 or 16 terms (the latter being simplified from an 18-term equation) [7]. Some statistical methods are utilized, such as ridge regression (RR) [7], truncated singular value decomposition (TSVD) [14], and partial least-squares regression (PLSR) [15] These methods render the estimated coefficients more accurate for the 18-term model but are not always effective for the 16-term model [7].
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