Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory

Abstract

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.

ULF wave power index for space weather and geophysical applications: A review

“…Algorithmic DMA approach enabled one to recognize low-amplitude geomagnetic pulsations of different types and their time limits [Zelinskiy et al, 2014]. What is important in our studies is that DMA-based methods have been implemented for an automated and unified anthropogenic anomaly recognition, such as spikes and jumps, in magnetograms from ground and satellite magnetometers [Bogoutdinov et al, 2010;Sidorov et al, 2012;Soloviev et al, 2009Soloviev et al, , 2012aSoloviev et al, , 2012b. These methods are applicable to both 1-minute and 1-second recordings, and are capable to operate continuously providing large data streams processing with high degree of reliability.…”

Geomagnetic activity during St. Patrick's Day storm inferred from global and local indicators

“…The DMA is based on the fuzzy logic and includes a series of algorithms aimed at basic tasks of data analysis. The DMA algorithms enable a morphological analysis of the time series, a detection of the formal anomalies and an estimation of the trends Soloviev et al, 2009Soloviev et al, , 2012aSoloviev et al, , 2012bSoloviev et al, , 2013Zelinskiy et al, 2014]. The DMA has been previously used for the geophysical monitoring [Agayan et al, 2016;Gvishiani et al, 2014Gvishiani et al, , 2016aGvishiani et al, , 2016b, Estimation of geomagnetic activity..., Annals of Geophysics, in press], for the data processing [Agayan et al, 2010[Agayan et al, , 2014Gvishiani et al, 2011], and for solution of various other problems which arise in the practice of geophysical data processing and interpretation.…”

Integration of data mining methods for Earth science data analysis in GIS environment

“…In particular, DMA includes a series of neo-clustering algorithms, which allow determining dense object condensations in multidimensional arrays and define their morphology (e.g., linear and ring structures), as well as the algorithms of anomaly recognition within noised time series. DMA has already found many successful applications in studying and recognizing anomalies in geological, geophysical and geodynamic data [Agayan and Soloviev, 2004;Bogoutdinov et al, 2010;Gvishiani et al, 2008Gvishiani et al, , 2014Sidorov et al, 2012;Soloviev et al, 2005Soloviev et al, , 2009Soloviev et al, , 2012aSoloviev et al, , 2012bSoloviev et al, , 2013Zelinskiy et al, 2014].…”