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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 7
Nat. Hazards Earth Syst. Sci., 11, 1863–1874, 2011
https://doi.org/10.5194/nhess-11-1863-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Progress in research on earthquake precursors

Nat. Hazards Earth Syst. Sci., 11, 1863–1874, 2011
https://doi.org/10.5194/nhess-11-1863-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Jul 2011

Research article | 07 Jul 2011

Signal discrimination of ULF electromagnetic data with using singular spectrum analysis – an attempt to detect train noise

S. Saito1, D. Kaida2,*, K. Hattori1, F. Febriani1, and C. Yoshino1 S. Saito et al.
  • 1Graduate School of Science, Chiba University, 1–33, Yayoi, Inage, Chiba 263-8522, Japan
  • 2Graduate School of Science and Technology, Chiba University, 1–33, Yayoi, Inage, Chiba 263-8522, Japan
  • *now at: Hewlett-Packard Japan, Ltd., Japan

Abstract. Electromagnetic phenomena associated with crustal activities have been reported in a wide frequency range (DC-HF). In particular, ULF electromagnetic phenomena are the most promising among them because of the deeper skin depth. However, ULF geoelctromagnetic data are a superposition of signals of different origins. They originated from interactions between the geomagnetic field and the solar wind, leak current by a DC-driven train (train noise), precipitation, and so on. In general, the intensity of electromagnetic signals associated with crustal activity is smaller than the above variations. Therefore, in order to detect a smaller signal, signal discrimination such as noise reduction or identification of noises is very important. In this paper, the singular spectrum analysis (SSA) has been performed to detect the DC-driven train noise in geoelectric potential difference data. The aim of this paper is to develop an effective algorithm for the DC-driven train noise detection.

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