As a part of the space environment monitor instrument suite, Geostationary Operational Environmental Satellite carries two boom-mounted magnetometers that measure the local magnetic field vector with a 0.5 second sampling rate. These data contain occasional baseline perturbations not of geophysical origin. One source of contamination is due to switching heaters that are installed along with each magnetometer and used to stabilize the temperature of the instrument. Detection of the heater induced field is complicated by the fact that in most cases these jumps are so small that they are hard to distinguish visually. In the present work we have developed the algorithm JM (from JUMP) aimed at automated and uniform recognition of jumps in GOES 2~Hz vector magnetic measurements. We present the performance of the JM~algorithm to a full day of measurements on 3 April 2010. On this date, almost all jumps were recognized by the JM~algorithm. The results demonstrate that the algorithm might be used to improve the existing data set from GOES~13, 14 and 15 series, and perhaps find use with the next generation of GOES satellites, beginning with GOES~16 launched on 19 November 2016.
GOES, satellite measurements, magnetic field, artificial disturbances, time series, magnetograms, pattern recognition, fuzzy logic, baseline jumps
1. Agayan, S., Bogoutdinov, S., Soloviev, A., Sidorov, R. The study of time series using the DMA methods and geophysical applications, // Data Sci. J., 2016. - v. 15 - no. 16 - p. 1.
2. Agayan, S. M., Bogoutdinov, Sh. R., Krasnoperov, R. I. Short introduction into DMA, // Russ. J. Earth Sci., 2018. - v. 18 - p. 1.
3. Balasis, G., et al. Magnetospheric ULF wave studies in the frame of Swarm mission: a time-frequency analysis tool for automated detection of pulsations inmagnetic and electric field observations, // Earth, Planets and Space, 2013. - v. 65 - p. 1385.
4. Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R. Fuzzy recognition of anomalies in time series, // Dokl. Earth Sci., 2008a. - v. 421 - no. 1 - p. 838.
5. Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R., Zlotnicki, J., Bonnin, J. Mathematical Methods of Geoinformatics: III. Fuzzy Comparisons and Recognition of Anomalies in Time Series, // Cybernet Sys. Analys., 2008b. - v. 44 - no. 3 - p. 309.
6. Gvishiani, A., et al. Survey of Geomagnetic Observations Made in the Northern Sector of Russia and New Methods for Analysing Them, // Surveys in Geophysics, 2014. - v. 35 - no. 5 - p. 1123.
7. Friis-Christensen, E., Lühr, H., Hulot, G. Swarm: A constellation to study the Earth's magnetic field, // Earth, Planets and Space, 2006. - v. 58 - p. 351.
8. Kulchinsky, R. G., Kharin, E. P., Shestopalov, I. P., Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R. Fuzzy logic methods for geomagnetic events detections and analysis, // Russ. J. Earth Sci., 2010. - v. 11 - no. 4 - p. 351.
9. Love, J. J., Chulliat, A. An international network of magnetic observatories, // Eos Trans. Am. Geophys. Union, 2013. - v. 94 - no. 42 - p. 373.
10. Mandrikova, O. V., Bogdanov, V. V., Solov'ev, I. S. Wavelet analysis of geomagnetic field data, // Geomagn. Aeron., 2013. - v. 53 - no. 2 - p. 268.
11. Ouadfeul, S. A., Tourtchine, V., Aliouane, L. Daily geomagnetic field prediction of INTERMAGNET observatories data using the multilayer perceptron neural network, // Arab. J. Geosci., 2015. - v. 8 - p. 268.
12. Reda, J., Fouassier, D. , Isac, A. , Linthe, H.-J. , Matzka, J., Turbitt, C. W. Improvements in geomagnetic observatory data quality // Geomagnetic Observations and Models, Vol. 5 - B.V: Springer Science + Business Media., 2011. - p. 127.
13. Singer, H. J., Matheson, L., Grubb, R., Newman, A., Bouwer, S. D. Monitoring space weather with the GOES magnetometers // GOES-8 and Beyond, Proc. SPIE, vol. 2812, edited by E. R. Washwell - Bellingham, Wash.: Int. Soc. for Opt. Eng.., 1996. - p. 299.
14. Soloviev, A., et al. Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory, // Earth, Planets and Space, 2012a. - v. 64 - no. 9 - p. 743.
15. Soloviev, A. A., Agayan, S. M. , Gvishiani, A. D. , Bogoutdinov, Sh. R., Chulliat, A. Recognition of disturbances with specified morphology in time series: Part 2. Spikes on 1-s magnetograms, // Izvestiya, Physics of the Solid Earth, 2012b. - v. 48 - no. 5 - p. 395.
16. Soloviev, A., Bogoutdinov, Sh. , Gvishiani, A. , Kulchinsky, R., Zlotnicki, J. Mathematical Tools for Geomagnetic Data Monitoring and the INTERMAGNET Russian Segment, // Data Sci. J., 2013. - v. 12 - p. WDS114.
17. Soloviev, A., Agayan, S., Bogoutdinov, S. Estimation of geomagnetic activity using measure of anomalousness, // Ann. Geophys., 2016. - v. 59 - no. 6 - p. WDS114.
18. Zhang, S., et al. Quality Control of Observation Data by the Geomagnetic Network of China, // Data Sci. J., 2016. - v. 15 - p. WDS114.
19. Zlotnicki, J., LeMouel, J.-L., Gvishiani, A., Agayan, S., Mikhailov, V., Bogoutdinov, Sh. Automatic fuzzy-logic recognition of anomalous activity on long geophysical records. Application to electric signals associated with the volcanic activity of la Fournaise volcano (Réunion Island), // Earth Planet. Sci. Lett., 2005. - v. 234 - p. 261.
