1. AUTOMATED QUALITY CONTROL OF GEOPHYSICAL TIME SERIES The algorithmic systems developed at Geophysical Center of Russian Academy of Sciences are intended for recognition of disturbances with defined morphology on time series. These algorithms were applied to 1-minute and 1-second INTERMAGNET data for recognition of artificial disturbances on the magnetograms. INTERMAGNET network is the basis for geomagnetic field monitoring so requirements for reliability of collected data are very high. Therefore, an important task is an objective and formalized recognition and further elimination of possible anthropogenic anomalies in data records. Discrete Mathematical Analysis (DMA) Scheme A spike is a chain of interrelated singular record fragments representing disturbances that are substantial vertically and insignificant horizontally and that do not lead to a shift of the recording level [Bogoutdinov et al., 2010]. Artificial disturbances on geomagnetic records Example of spike recognition on 1-minute data (FRD, X, 2005) Jump recognition on 1-minute data Jump recognition on satellite magnetic data (GOES, 2 Hz) The results of training and testing show that SP, SPs and JM algorithms are efficient enough to recognize almost all artificial spikes and jumps detected by data experts manually. This also provides the possibility to carry out retrospective analysis and quality control of the magnetograms available at ICSU World Data Centers. SP algorithm block scheme Brute-force search of free parameter values: 4 600 sets of values Spike recognition on 1-minute INTERMAGNET magnetograms A.A. Soloviev 1, A. Chulliat 2, R.V. Sidorov 1, Sh.R. Bogoutdinov 1 1-Geophysical Center RAS, Moscow, Russia; 2 – Insitiut de Physique du Globe de Paris, France SP algorithm recognition results Jump recognition on INTERMAGNET magnetograms SPs recognition statistics Sh.R. Bogoutdinov, A.D. Gvishiani, S.M. Agayan, A.A. Solovyev, E. Kihn, Recognition of Disturbances with Specified Morphology in Time Series. Part 1: Spikes on Magnetograms of the Worldwide INTERMAGNET Network, Izvestiya, Physics of the Solid Earth, 2010, Vol. 46, No. 11, pp. 1004–1016 A. Soloviev, A. Chulliat, S. Bogoutdinov, A. Gvishiani, S. Agayan, A. Peltier, B. Heumez (2012), Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory, Earth Planets Space, Vol. 64 (No. 9), pp. 743-752, 2012, doi:10.5047/eps.2012.03.004 Example of spike recognition on 1-second data Comparison with classical methods SPs algorithm block scheme Spike recognition on 1-second magnetograms Comparison with  F method Comparison with statistical algorithms Components XYZ and intensity F Period Number of obser- vatories Events recognized Target miss False alarm Learning200772900%5.2% Exam 1200851101.0%8.2% Exam 22003*710320%15.4% Exam 32005*75350.2%14.6% * Increased magnetic activity Jump is an anomaly on a record leading to its baseline shift. JM Algorithm: Calculating measures of jumpiness using fuzzy bounds where rinf, linf are the fuzzy lower bounds, rsup, lsup are the fuzzy upper bounds, Potential jump (red), wings (green), fuzzy bounds (black) References: [Soloviev et al., 2012] ~ 140 days Natural geomagnetic pulsations on 1-second data