Automated quality control of geophysical time series

1. A.A. Soloviev1, A. Chulliat2, R.V. Sidorov1, Sh.R. Bogoutdinov1 1-Geophysical Center RAS, Moscow, Russia; 2 – Insitiut de Physique du Globe de Paris, France Discrete Mathematical Analysis (DMA) Scheme 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. Artificial disturbances on geomagnetic records Example of spike recognition on 1-second data Spike recognition on 1-minute INTERMAGNET magnetograms 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]. Automated quality control of geophysical time series SP algorithm block scheme Example of spike recognition on 1-minute data (FRD, X, 2005) SP algorithm recognition results SPs algorithm block scheme Brute-force search of free parameter values: 4 600 sets of values ~ 140 days Jump recognition on INTERMAGNET magnetograms Spike recognition on 1-second magnetograms Jump is an anomaly on a record leading to its baseline shift. Calculating measures of jumpiness using fuzzy bounds JM Algorithm: where rinf, linf are the fuzzy lower bounds, rsup, lsup are the fuzzy upper bounds, Natural geomagnetic pulsationson 1-second data Jump recognition on 1-minute data Comparison with classical methods Potential jump (red), wings (green), fuzzy bounds (black) Jump recognition on satellite magnetic data (GOES, 2 Hz) [Soloviev et al., 2012] Comparison with statistical algorithms Comparison with F method SPs recognition statistics 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. References: 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