EventsThe 8th International Electronic Conference on Atmospheric Sciences
Published
This submission belongs to the session S6. Upper Atmosphere of the event The 8th International Electronic Conference on Atmospheric Sciences
Published date
09 Oct, 2026
Academic Editor
author-avatarGeorge Balasis
Citation
Sudipta Sasmal, Masashi Hayakawa, Kousik Nanda, Yasuhide Hobara, Stelios M Potirakis, Abhirup Datta, Shih‑Sian Yang, Multichannel Characterization of Seismogenic Perturbations within the Lithosphere–Atmosphere–Ionosphere Coupling Framework, in Proceedings of The 8th International Electronic Conference on Atmospheric Sciences, 14 October–16 October 2026, MDPI: Basel, Switzerland
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Multichannel Characterization of Seismogenic Perturbations within the Lithosphere–Atmosphere–Ionosphere Coupling Framework

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Kousik Nanda 1,3
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1. Department of Space and Atmospheric Science, Institute of Astronomy Space and Earth Science, Kolkata, 700054, India
2. QuakeInsight Tokyo, UEC (The University of Electro‑Communications), Tokyo, 182‑0026, Japan
3. Department of Physics (UG & PG), Prabhat Kumar College, Contai, 721401, India
4. Graduate School of Informatics and Engineering, UEC (The University of Electro‑Communications), Tokyo, 182‑8585, Japan
5. Department of Electrical and Electronics Engineering, University of West Attica, Egaleo, 12241, Greece
6. Department of Astronomy, Astrophysics and Space Engineering, Indian Institute of Technology Indore, Indore, 453552, India
7. Environmental Research and Information Center, Chang Jung Christian University, Tainan, 711301, Taiwan
Abstract

The Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) framework provides a physical basis for investigating temporally ordered disturbances associated with earthquake preparation from the lower atmosphere to the near-Earth plasma environment. We examine whether seismogenic effects can be distinguished from environmental variability through coherent, sequential responses across independent thermal, acoustic, and electromagnetic channels. Representative cases include the 2020 Samos (M 6.9), 2018 Hokkaido (M 6.7), 2018 Aomori (M 6.2), 2021 Tokyo (Mw 5.9), and 2025 Kamchatka (Mw 8.8) earthquakes, supplemented by statistical GNSS-TEC analyses of the 2021 Haiti and 2022 Cyprus events. Observables include near-surface thermal parameters, atmospheric-gravity-wave (AGW) activity, VLF/LF propagation, GNSS-TEC, and satellite plasma, magnetic-field, and energetic-particle measurements. Detection combines moving mean/standard deviation, median/MAD indices, percentile thresholds, persistence tests, wavelet analysis, event–background comparison, and cross-channel temporal coincidence. GPS-TEC analysis further applies diurnal removal, Gaussian, MAD, and quantile detection, median-normalized occurrence/energy indices, and a 95th-percentile filter. We evaluate meteorological and space-weather contamination using weather and geomagnetic indices, excluding unsuitable orbital regions, the South Atlantic Anomaly, and, where appropriate, disturbed intervals. For Samos, TEC deviations reached ~4.5 and 2.5 TECU; AGW potential energy increased 6–8 days beforehand at ~46–48 km, with 65–110 min TEC disturbances, NOAA-15 energetic-particle enhancements ~10 and 4 days before, and Swarm irregularities ~1 day before. For Kamchatka, thermal disturbances intensified ~1–2 days, AGWs ~3–5 days, and VTEC ~2–3 days before the mainshock; AGW energy reached ~18–20 J kg⁻¹ versus 8–12 J kg⁻¹ background, and VTEC ~10–15 TECU versus 0–3 TECU. Hokkaido VLF anomalies reached ~−2σ to −3.5σ and were independently reproduced by NARMAX/LSTM analysis. Tokyo LWPC modeling indicated a ~3 km reflection-height decrease and ~0.11 km⁻¹ sharpness increase. Overall, temporal ordering and cross-layer coherence provide stronger evidence than isolated anomalies, supporting probabilistic, event-dependent LAIC associations rather than deterministic earthquake prediction.

Keywords
Lithosphere–Atmosphere–Ionosphere Coupling
Seismogenic Perturbations
Thermal–Acoustic–Electromagnetic Channels
Multiparametric Analysis
Automated Anomaly Detection
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