@inproceedings{644bc7f1866a41d3a7ab04b80d08c089,
title = "Regional Geomagnetic Map Construction based on Support Vector Machine Residual Kriging",
abstract = "Regional geomagnetic maps are widely used in geomagnetic navigation and magnetic anomaly detection. However, the complexity of geomagnetic spatial trend changes and the spatial sparseness of the geomagnetic data affect the accuracy of regional geomagnetic map construction. In order to improve the accuracy of regional geomagnetic maps, this paper proposes the Support Vector Machine Residual Kriging method (SVMRKriging). First, Support Vector Machine (SVM) is used to fit the geomagnetic trend changes, then the residual component is interpolated by ordinary Kriging, and finally these two parts are added to construct a regional geomagnetic map. Experiments were performed using geomagnetic grid data and aeromagnetic data. The experiment results show that SVMRKriging method can improve the accuracy of regional geomagnetic maps with geomagnetic trend changes.",
keywords = "Kriging, Regional Geomagnetic Map, Support Vector Machine",
author = "Tong Liu and Xingyu Li and Mengyin Fu and Zhaoxiang Liang",
note = "Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9188420",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "3500--3504",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
address = "United States",
}