IonKit-NH: A MATLAB-based toolkit for ionospheric detection of earthquake, tsunami and volcanic eruption

Long Tang

Earthquake Research Advances ›› 2025, Vol. 5 ›› Issue (2) : 1 -4.

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Earthquake Research Advances ›› 2025, Vol. 5 ›› Issue (2) :1 -4. DOI: 10.1016/j.eqrea.2024.100353
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IonKit-NH: A MATLAB-based toolkit for ionospheric detection of earthquake, tsunami and volcanic eruption

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Abstract

In recent years, GNSS-derived total electron content (TEC) measurements have emerged as an effective method for detecting natural hazards through their ionospheric manifestations. Seismo-atmospheric disturbances generated by earthquakes, tsunamis, and volcanic eruptions propagate as traveling ionospheric disturbances (TIDs) that modify ionospheric electron density. Despite this potential, specialized open-source tools for such analyses remain limited. We present IonKit-NH, a MATLAB-based toolkit enabling systematic processing of multi-GNSS data (GPS, GLONASS, Galileo, BDS) through dual-frequency combination analysis for TEC derivation. The software implements automated generation of time-distance diagrams and 2D TEC perturbation maps, enabling quantitative characterization of TID propagation parameters associated with natural hazards. This toolkit enhances standardized analysis of ionospheric precursors and co-seismic signals across global navigation satellite systems.

Keywords

Natural hazards / Earthquakes / GNSS / Total electron content (TEC) / Travelling ionospheric disturbances (TIDs)

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Long Tang. IonKit-NH: A MATLAB-based toolkit for ionospheric detection of earthquake, tsunami and volcanic eruption. Earthquake Research Advances, 2025, 5(2): 1-4 DOI:10.1016/j.eqrea.2024.100353

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CRediT authorship contribution statement

Long Tang: Writing - original draft, Writing - review & editing, Software, Resources, Methodology, Investigation, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author agreement and acknowledgment

The GNSS data used in this study are provided by the Geographical Survey Institute, Japan (http://www.gsi.go.jp/). This study was supported by National Natural Science Foundation of China (Grant No. 42274017), and Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023A1515030184).

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