Work

Classifying Seismic Events Linked to Solar Activity: A Retrospective LSTM Approach Using Proton Density

Year: 2024

Type: article

Abstract: The influence of solar activity on seismic activity is a subject of debate. Previous studies have shown that there is sometimes a correlation and sometimes a contradiction between solar activity maxima and large earthquakes. Long-term memory neural network is used to study the relationship between solar activity and seismic activity. This study emphasizes retrospective classification rather than direct prediction, refining the LSTM architecture to maximize classification accuracy and processing data from the Solar and Heliospheric Observatory and the U.S. Geological Survey earthquake catalogs. A declustering technique is used to select large seismic events and weighted learning corrects for class imbalances. The LSTM model accurately classified earthquakes (84.47%) and proton density variations. The results support the theory that solar activity, in particular proton density, can anticipate earthquake events.

Source: Atmosphere

DOI: 10.3390/atmos15111290

Authors: Aizhan Altaibek, Marat Nurtas, Zhumabek Zhantayev, Beibit Zhumabayev, Ayazhan Kumarkhanova

Institutions: International Information Technologies University, Ionosphere Institute, Kazakh-British Technical University

Language: English

Topic: Earthquake Detection and Analysis

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,728