Nonstationary analysis of geomagnetic time sequences from Mount Etna and North Palm Springs earthquake

Abstract

Volcanomagnetic and/or seismomagnetic effects are geomagnetic variations generated before eruptions and/or seismic events. Our aim is to analyze geomagnetic time series to detect the volcanomagnetic and/or seismomagnetic effects among a number of other variations. Two advanced signal‐processing techniques are proposed to analyze the geomagnetic time series. The first technique, called Continuous Wavelet Transform Singularity Analysis (CWTSA), is based on the Continuous Wavelet Transform; the second, called Time‐Variant Statistical Analysis of Nonstationary Signals (TVANS), is based on a time‐varying adaptive algorithm (Recursive Least Squares). Both techniques are very effective in detecting the geomagnetic variations at the time instants likely linked to volcanic and/or seismic activity. The application of these methodologies to geomagnetic time sequences, respectively, recorded on Mount Etna during the volcanic activity of 1981 and in North Palm Springs during the seismic events of 8 July 1986 yields a good correspondence between events detected by both techniques and volcanic end seismic events. The statistical significance of geomagnetic time series was also assessed to verify the obtained results from CWTSA and TVANS. It was defined at significance level of 95% in the wavelet power spectrum for the difference of the geomagnetic time series aiming at distinguishing the most “significant” events when they are upon this one.

Radon variability at the Elat granite, Israel: Heteroscedasticity and nonlinearity

“…In practice, the investigation of radon temporal variability requires nonlinear approaches, as used by Fedi et al [2003] in the analysis of geomagnetic data. Therefore, dispersion around the mean level increases faster (slower) relative to the mean for low (high) Rn.…”

“…The statement that Rn time series from Elat are nonlinear has both practical and geophysical implications. In practice, the investigation of radon temporal variability requires nonlinear approaches, as used by Fedi et al [2003] in the analysis of geomagnetic data. Results from methods assuming stationarity and/or linearity (e.g.…”

Wavelet Analysis as a Tool for Revealing Geomagnetic Precursors of the Zhangbei Earthquake

“…Alperovich and Zheludev [2] analyzed the geomagnetic field data of 1989 Loma-Prieta earthquake (M =7.0) by wavelet packet analysis and stationary wavelet analysis, and they got not only the geomagnetic abnormal signals before the earthquake but also the main frequency band as well as the moving speed of the signal. Some other similar research work [3,4] using wavelet analysis also revealed successfully the geomagnetic abnormal signals before earthquakes. However, the above-mentioned research work mainly focused on the magnitude of abnormal signals.…”

Composite continuous wavelet transform of potential fields with different choices of analyzing wavelets

“…Such analysis is also well known in geomagnetism [ Alexandrescu et al , 1995], seismics [ Chakraborty and Okaya , 1995; Chamoli et al , 2010b] or in many other scientific frameworks, such as image analysis [e.g., Mallat , 1998]; edge detection [ Mallat and Hwang , 1992]; Holder exponent estimation [ Fedi et al , 2003] and so on. So, methods using the continuous wavelet transform belong to the class of the multiscale methods for the source parameters estimation [ Fedi , 2007].…”