Citation: Zhou, S.; Yang, C.; Su, Z.;
Yu, P.; Jiao, J. An Aeromagnetic
Compensation Algorithm Based on
Radial Basis Function Artificial
Neural Network. Appl. Sci. 2023, 13,
136. https://doi.org/10.3390/
app13010136
Academic Editors:
Andrzej Łukaszewicz,
Wojciech Giernacki,
Zbigniew Kulesza, Jaroslaw Pytka
and Andriy Holovatyy
Received: 22 November 2022
Revised: 16 December 2022
Accepted: 16 December 2022
Published: 22 December 2022
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
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conditions of the Creative Commons
Attribution (CC BY) license (https://
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4.0/).
Article
An Aeromagnetic Compensation Algorithm Based on Radial
Basis Function Artificial Neural Network
Shuai Zhou
1
, Changcheng Yang
1
, Zhenning Su
2
, Ping Yu
1
and Jian Jiao
1,
*
1
College of GeoExploration Science and Technology, Jilin University, Changchun 130012, China
2
Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geoscience,
Langfang 065000, China
* Correspondence: jiaojian001@jlu.edu.cn
Abstract:
Aeromagnetic exploration is a magnetic exploration method that detects changes of the
earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magne-
tometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic
surveying plays an increasingly important role in mineral exploration and other fields due to its
advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due
to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude,
magnetic field interference will occur and affect the measurement of the geomagnetic field by the
magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic
interference and improve the magnetic measurement accuracy of the magnetometer. This paper
focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy
of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical
simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out,
and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the
first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN,
the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has
higher compensation accuracy and stronger generalization ability.
Keywords:
aeromagnetic compensation; radial basis function; deep learning; unmanned aerial
vehicles (UAV); local minimum
1. Introduction
With the development of the global economy, the demand for mineral resources in
all countries in the world is also increasing. However, due to complex terrain conditions,
many areas rich in mineral resources cannot be explored. In order to increase the detection
range and improve exploration efficiency, aeromagnetic measurement technology has been
rapidly developed. Airborne magnetic surveying is an important airborne geophysical ex-
ploration method, which can be used for magnetic data acquisition under various complex
terrain conditions.
Moreover, UAV technology has developed very rapidly and has been well used in all
walks of life, so UAV survey technology has gradually developed, and is now widely used
in resource exploration, regional survey and other fields [
1
,
2
]. With the development of
UAV technology, more and more countries have carried out the research and development
of UAV aeromagnetic measurement equipment technology and achieved remarkable results.
The available information indicates that the first company in the world to develop UAV
aeromagnetic survey equipment was Magsurvey in the United Kingdom, which developed
the PrionUAV aeromagnetic survey system in 2003 [
3
]. Since then, many companies around
the world have conducted research and development of UAV aeromagnetic survey systems,
such as the GeoRanger-I of the Dutch company Fugro [
4
], the Canadian company Universal
Wing Geophysical (UWG) Venturer [
5
], the Japanese RMAX-G1 [
6
], the Swiss and German
Appl. Sci. 2023, 13, 136. https://doi.org/10.3390/app13010136 https://www.mdpi.com/journal/applsci
