In the present paper we apply a recently developed pattern recognition algorithm SPs to the problem of automated detection of artificial disturbances in one-second magnetic observatory data. The SPs algorithm relies on the theory of discrete mathematical analysis, which has been developed by some of the authors for more than 10 years. It continues the authors' research in the morphological analysis of time series using fuzzy logic techniques. We show that, after a learning phase, this algorithm is able to recognize artificial spikes uniformly with low probabilities of target miss and false alarm. In particular, a 94% spike recognition rate and a 6% false alarm rate were achieved as a result of the algorithm application to raw one-second data acquired at the Easter Island magnetic observatory. This capability is critical and opens the possibility to use the SPs algorithm in an operational environment.

Abstract. We propose a new algorithm for calibrating definitive observatory data with the goal of providing users with estimates of the data error standard deviations (SDs). The algorithm has been implemented and tested using Chambonla-Forêt observatory (CLF) data. The calibration process uses all available data. It is set as a large, weakly non-linear, inverse problem that ultimately provides estimates of baseline values in three orthogonal directions, together with their expected standard deviations. For this inverse problem, absolute data error statistics are estimated from two series of absolute measurements made within a day. Similarly, variometer data error statistics are derived by comparing variometer data time series between different pairs of instruments over few years. The comparisons of these time series led us to use an autoregressive process of order 1 (AR1 process) as a prior for the baselines. Therefore the obtained baselines do not vary smoothly in time. They have relatively small SDs, well below 300 pT when absolute data are recorded twice a week -i.e. within the daily to weekly measures recommended by INTERMAGNET. The algorithm was tested against the process traditionally used to derive baselines at CLF observatory, suggesting that statistics are less favourable when this latter process is used. Finally, two sets of definitive data were calibrated using the new algorithm. Their comparison shows that the definitive data SDs are less than 400 pT and may be slightly overestimated by our process: an indication that more work is required to have proper estimates of absolute data error statistics. For magnetic field modelling, the results show that even on isolated sites like CLF observatory, there are very localised signals over a large span of temporal frequencies that can be as large as 1 nT. The SDs reported here encompass signals of a few hundred metres and less than a day wavelengths.

Abstract. During magnetic observatory data acquisition, the data time stamp is kept synchronized with a precise source of time. This is usually done using a GPS-controlled pulse per second (PPS) signal. For some observatories located in remote areas or where internet restrictions are enforced, only the magnetometer data are transmitted, limiting the capabilities of monitoring the acquisition operations. The magnetic observatory in Lanzhou (LZH), China, experienced an unnoticed interruption of the GPS PPS starting 7 March 2013. The data logger clock drifted slowly in time: in 6 months a lag of 27 s was accumulated. After a reboot on 2 April 2014 the drift became faster, −2 s per day, before the GPS PPS could be restored on 8 July 2014. To estimate the time lags that LZH time series had accumulated, we compared it with data from other observatories located in East Asia. A synchronization algorithm was developed. Natural sources providing synchronous events could be used as markers to obtain the time lag between the observatories. The analysis of slices of 1 h of 1 s data at arbitrary UTC allowed estimating time lags with an uncertainty of ∼ 11 s, revealing the correct trends of LZH time drift. A precise estimation of the time lag was obtained by comparing data from co-located instruments controlled by an independent PPS. In this case, it was possible to take advantage of spikes and local noise that constituted precise time markers. It was therefore possible to determine a correction to apply to LZH time stamps to correct the data files and produce reliable 1 min averaged definitive magnetic data.