Recognition of disturbances with specified morphology in time series: Part 2. Spikes on 1-s magnetograms
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Preliminary magnetograms contain different types of temporal anthropogenic disturbances: spikes, baseline jumps, drifts, etc. These disturbances should be identified and filtered out during the preprocessing of the preliminary records for the definitive data. As of now, at the geomagnetic observatories, such filtering is carried out manually. Most of the disturbances in the records sampled every second are spikes, which are much more abundant than those on the magnetograms sampled every minute. Another important feature of the 1-s magnetograms is the presence of a plenty of specific disturbances caused by short-period geomagnetic pulsations, which must be retained in the definitive records. Thus, creating an instrument for formalized and unified recognition of spikes on the preliminary 1-s magnetograms would largely solve the problem of labor-consuming manual preprocessing of the magnetic records. In the context of this idea, in the present paper, we focus on recognition of the spikes on the 1-s magnetograms as a key point of the problem. We describe here the new algorithm of pattern recognition, SPs, which is capable of automatically identifying the spikes on the 1-s magnetograms with a low probability of missed events and false alarms. The algorithm was verified on the real magnetic data recorded at the French observatory located on Easter Island in the Pacific.
KeywordsFalse Alarm Solid Earth Geomagnetic Pulsation Thick Black Line Geomagnetic Observatory
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- Aivazyan, S.A., Statisticheskoe issledovanie zavisimostei (Statistical Study of Dependences), Moscow: Metallurgiya, 1968.Google Scholar
- Aivazyan, S.A., Enyukov, I.S., and Meshalkin, L.D., Prikladnaya statistika. Osnovy modelirovaniya i pervichnaya obrabotka dannykh (Applired Statistics: Basics of Modeling and Preprocessing of the Data), Moscow: Finansy i statistika, 1983.Google Scholar
- Chulliat, A., Lalanne, X., Gaya-Pique, L.R., Truong, F., and Savary. J., The New Easter Island Magnetic Observatory, in Proc. of the XIII IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing, Love, J.J. (ed.), U.S. Geological Survey Open-File Report 2009-1226, 2009.Google Scholar
- Gvishiani, A.D., Agayan, S.M., and Bogoutdinov, Sh.R., Fuzzy Recognition of Anomalies in Time Series, Dokl. Akad. Nauk, 2008, vol. 421, no. 5, pp. 838–842.Google Scholar
- Gvishiani, A.D., Agayan, S.M., Bogoutdinov, Sh.R., and Solovyov, A.A., Discrete Mathematical Analysis and Applications Geology and Geophysics, Vestnik KRAUNTs. Nauki O Zemle, 2010, vol. 16, no. 2, pp. 106–125.Google Scholar
- Kleimenova, N.G., Geomagnetic Pulsations, in Modeli kosmosa (Space Models), Moscow: MGU, 2007.Google Scholar
- Knuth, D., The Art of Computer Programming. Volume 3: Sorting and Searching, Massachusetts, Addison-Wesley, 1968.Google Scholar
- Soloviev, A.A., Bogoutdinov, Sh.R., Agayan, S.M., Gvishiani, A.D., and Kihn, E., Detection of Hardware Failures at INTERMAGNET Observatories: Application of Artificial Intelligence Techniques to Geomagnetic Records Study, Russ. J. Earth Sci., 2009, vol. 11, ES2006. doi: 10.2205/2009ES000387Google Scholar
- Soloviev, A., Chulliat, A., Bogoutdinov, S., Gvishiani, A., Agayan, S., Peltier, A., and Heumez, B., Automated Recognition of Spikes on 1 Hz Data Recorded at the Easter Island Magnetic Observatory, Earth Planet. Space (in press).Google Scholar