Predicting earthquakes by anomalies in the ionosphere

Standard

Predicting earthquakes by anomalies in the ionosphere. / Chaplygina, Daria ; Grafeeva, Natalia.

In: CEUR Workshop Proceedings, Vol. 2691, 01.01.2020.

Research output: Contribution to journal › Conference article › peer-review

BibTeX

@article{66c2fbb06f924a44a310392662ad58d7,

title = "Predicting earthquakes by anomalies in the ionosphere",

abstract = "Since earthquakes are a global-scale problem, humanity has been attempting to predict them for a long time. Earlier [1], it was shown that machine learning can be used to predict earthquakes. Nevertheless, a sufficiently accurate and complete predictive model could not be obtained, which may be due to an insufficient number of features. In this paper, anomalies in the ionosphere preceding seismic activity are considered as earthquake precursors. Two existing approaches to detecting ionosphere anomalies were considered; a third one was proposed, using readings of several ionosondes located in the neighborhood of the earthquake area or in a ring around such neighborhood. To test these approaches, a collection of ionosphere characteristics data, obtained from ground ionosondes, was gathered and processed. In the future, discovered anomalies are planned to be used as features for machine learning models.",

keywords = "Data mining, Earthquake prediction, Ionosphere anomalies, Seismology, Time series",

author = "Daria Chaplygina and Natalia Grafeeva",

year = "2020",

month = jan,

day = "1",

language = "English",

volume = "2691",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "RWTH Aahen University",

note = "Fifth Conference on Software Engineering and Information Management 2020, SEIM 2020 ; Conference date: 16-05-2020",

}

RIS

TY - JOUR

T1 - Predicting earthquakes by anomalies in the ionosphere

AU - Chaplygina, Daria

AU - Grafeeva, Natalia

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Since earthquakes are a global-scale problem, humanity has been attempting to predict them for a long time. Earlier [1], it was shown that machine learning can be used to predict earthquakes. Nevertheless, a sufficiently accurate and complete predictive model could not be obtained, which may be due to an insufficient number of features. In this paper, anomalies in the ionosphere preceding seismic activity are considered as earthquake precursors. Two existing approaches to detecting ionosphere anomalies were considered; a third one was proposed, using readings of several ionosondes located in the neighborhood of the earthquake area or in a ring around such neighborhood. To test these approaches, a collection of ionosphere characteristics data, obtained from ground ionosondes, was gathered and processed. In the future, discovered anomalies are planned to be used as features for machine learning models.

AB - Since earthquakes are a global-scale problem, humanity has been attempting to predict them for a long time. Earlier [1], it was shown that machine learning can be used to predict earthquakes. Nevertheless, a sufficiently accurate and complete predictive model could not be obtained, which may be due to an insufficient number of features. In this paper, anomalies in the ionosphere preceding seismic activity are considered as earthquake precursors. Two existing approaches to detecting ionosphere anomalies were considered; a third one was proposed, using readings of several ionosondes located in the neighborhood of the earthquake area or in a ring around such neighborhood. To test these approaches, a collection of ionosphere characteristics data, obtained from ground ionosondes, was gathered and processed. In the future, discovered anomalies are planned to be used as features for machine learning models.

KW - Data mining

KW - Earthquake prediction

KW - Ionosphere anomalies

KW - Seismology

KW - Time series

UR - http://www.scopus.com/inward/record.url?scp=85092442628&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85092442628

VL - 2691

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - Fifth Conference on Software Engineering and Information Management 2020

Y2 - 16 May 2020

ER -

ID: 103097959