Szczegóły publikacji
Opis bibliograficzny
Extremely Low Frequency (ELF) electromagnetic signals as a possible precursory warning of incoming seismic activity / Vasilis Tritakis, Janusz MŁYNARCZYK, Ioannis Contopoulos, Jerzy Kubisz, Vasilis Christofilakis, Giorgos Tatsis, Spyridon K. Chronopoulos, Christos Repapis // Atmosphere [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2073-4433. — 2024 — vol. 15 iss. 4 art. no. 457, s. 1–15. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12–15, Abstr. — Publikacja dostępna online od: 2024-04-07
Autorzy (8)
- Tritakis Vasilis
- AGHMłynarczyk Janusz
- Contopoulos Ioannis
- Kubisz Jerzy
- Christofilakis Vasilis
- Tatsis Giorgos
- Chronopoulos Spyridon K.
- Repapis Christos
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 153953 |
|---|---|
| Data dodania do BaDAP | 2024-06-27 |
| Tekst źródłowy | URL |
| DOI | 10.3390/atmos15040457 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Atmosphere |
Abstract
We analyzed a large number (77) of low-to-medium-magnitude earthquakes (M3.5–M6.5) that occurred within a period of three years (2020–2022) in the Southern half of Greece in relation to the ELF activity in that region and time period. In most cases, characteristic ELF signals appear up to 20 days before the earthquakes. This observation may add an important new element to the Lithospheric–Atmospheric–Ionospheric scenario, thus contributing to a better prediction of incoming earthquakes. We discuss the role of ELF observations in reliable seismic forecasting. We conclude that the magnitude of an earthquake larger than M4.0 and the distance of the epicenter shorter than 300 km from the recording site is needed for typical pre-seismic signals to be observed. Finally, we remark that a reliable prediction of earthquakes could result from an integrated project of multi-instrumental observations, where all the known variety of precursors would be included, and the whole data set would be analyzed by advanced machine learning methods.
