Improper handling of missing data and offsets will affect the accuracy of a signal of interest. The trend in GNSS belonging to GLOSS is key to getting the absolute sea level. However, this is affected by the periodic signals that are included. Although adaptive EEMD is capable of extracting periodic signals, missing data and offsets are ignored in previous work. Meanwhile, the time-varying periodic characteristics derived by adaptive EEMD are more conducive to analyzing the driving factors.

Understanding volcanic activity is crucial for uncovering the fundamental physical mechanisms governing this natural phenomenon. In this study, we show the application of multifractal and statistical analysis, to investigate changes associated with volcanic activity. We aim to identify significant variations within the physical processes related to changes in volcanic activity. These methodologies offer the potential to identify pertinent changes preceding a high-energy explosion.

Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series, together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series after deterministically subtracting geophysical signals from the observations with the Lévy processes.