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The modern knowledge in the field of many geosciences is based on permanent observations during the last decades. These data are represented by various long time series of the observed natural changes. They consist mainly of quasi harmonic oscillations with variable phases and amplitudes and small irregular variations, containing abrupt changes due to certain natural sources of impulse excitations. These variations may be due to various geophysical impulses and in some cases to the inaccuracy of data collection from the many different instruments spread on all over the world. The natural sources of the observed quasi impulse variations with duration from seconds to days are earthquakes, hurricanes and tornadoes, geomagnetic jerks, landslides, solar eruptions etc. The data jumps in the long time series may be due to the inaccuracy of classical instrumental techniques or to the changes of the networks arising from the adding or removing the observational stations and replacement of the instruments. The direct determination of the data jumps is a difficult task, because the jumps are very small in relation to the amplitudes of the seasonal, interannual, short and long periodic terms of the observed variations. A new high-sensitive method of small jumps determination in long time series has been formulated recently (Chapanov et al., 2014). This method detects any sudden changes of the mean data value and of the linear trend, whose magnitude is significantly less than the level of the random noise. The accuracy and sensitivity of this method is evaluated by simulated time series of polar motion, VLBI pole offset, GPS and climatic data. The accuracy of the method is determined by the differences between the initial and estimated values of data jumps. The sensitivity of the method is determined as a level of jumps, detectable for any specific data collection.

Ocuracy and sensitivity of a method of JUMP detection, evaluated by simulated time series