Name
Abstract | ||
|---|---|---|
We analyzed an extended time series of Schumann Resonance recordings with two multi-parametric statistical methods, the generalized linear Logistic Regression-LogReg and the non-linear Random Forest-RF, in order to test their potential for earthquake prediction within a narrow time-space-magnitude window of 48 h, 250 km from our observing site, and events higher than magnitude 4 of the Richter scale. The LogReg method identified the power of the signal within our 10-min recording intervals as the main seismic precursor parameter. The RF method obtained promising results that will improve with continuous enrichment of the running data sample with new data. We conclude that a systematic analysis of Schumann Resonance recordings may lead to satisfactory levels of seismic prediction. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s12145-020-00535-9 | EARTH SCIENCE INFORMATICS |
Keywords | DocType | Volume |
Schumann resonances, ELF electromagnetic emissions, Earthquake precursors, Logistic regression model, Random Forest model | Journal | 14 |
Issue | ISSN | Citations |
1 | 1865-0473 | 0 |
PageRank | References | Authors |
0.34 | 2 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Florios | 1 | 0 | 0.34 |
| I. Contopoulos | 2 | 0 | 0.34 |
| tatsis giorgos | 3 | 10 | 2.70 |
| vasilis christofilakis | 4 | 9 | 3.35 |
| Spyridon K. Chronopoulos | 5 | 16 | 5.50 |
| C. Repapis | 6 | 0 | 0.34 |
| Vasilis Tritakis | 7 | 1 | 0.82 |