Journal of Geodesy and Geoinformation Science ›› 2021, Vol. 4 ›› Issue (1): 94-108.doi: 10.11947/j.JGGS.2021.0112

• Special Issue • Previous Articles     Next Articles

Automatic Anomaly Detection for Swarm Observations

Yaxin BI1(),Vyron CHRISTODOULOU1,George WILKIE1,Guoze ZHAO3,Peter NICHOLL1,Mingjun HUANG2,Bin HAN3,Ji TANG3   

  1. 1. School of Computing, Faculty of Computing, Engineering and the Built Environment, Ulster University, Newtownabbey, BT36 7, UK
    2. School of Architecture and the Built Environment, Ulster University, Newtownabbey, BT36 7, UK
    3. Institute of Geology, China Earthquake Administration, Beijing 100029, China
  • Received:2020-10-10 Accepted:2020-12-25 Online:2021-03-20 Published:2021-04-06
  • About author:Yaxin BI, male, majors in machine learning. E-mail: y.bi@ulster.ac.uk
  • Supported by:
    National Natural Science Foundation of China(41374077)

Abstract:

The Swarm satellite mission was launched on 22 November 2013, it is the first European Space Agency’s constellation of three satellites, dedicated to monitoring geomagnetic field changes. The measurements delivered by the three satellites are very valuable for a range of applications, including the earthquake prediction study. However, for more than 5 years, relatively little advancement has been achieved in establishing a systematic approach for detecting anomalies from the satellite measurements for predicting earthquakes. This paper presents the challenges of developing a pragmatic framework for automatic anomaly detection and highlights innovative features of functional components developed. Through a case study we demonstrate a functionality pipeline of the system in detecting anomalies, and present our solutions to coping with data sparsity and parameter tuning as well as insights into the differences between discovering seismic anomalies from periodic and non-periodic data observed by the Swarm satellites.

Key words: anomaly detection; Swarm satellites; earthquake prediction study