IAG-IASPEI 2017

Presentation information

Oral

IASPEI Symposia » S11. Geo & space technologies to study pre-earthquake processes: Observation, modeling, forecasting

[S11-1] Geo & space technologies to study pre–earthquake processes: Observation, modeling, forecasting I

Wed. Aug 2, 2017 8:30 AM - 10:00 AM Room 503 (Kobe International Conference Center 5F, Room 503)

Chairs: Dimitar Ouzounov (Chapman University) , Katsumi Hattori (Chiba University)

9:00 AM - 9:15 AM

[S11-1-03] Characteristics of Ionospheric Electron Distribution for large Earthquakes around Japan

Katsumi Hattori1, Mustafa Yagmur1, Shinji Hirooka1, Jann-Yenq Liu2 (1.Chiba University, Japan, 2.National Central University, Taiwan)

invited

Pre-seismic electron density anomalies have been widely discussed phenomena in ionospheric studies. However, it is not well-known what causes these anomalies. The other question is how to distinguish ionospheric anomalies from other disturbances such as geomagnetic storms. Therefore, a characterization and classification of magnetic storm and earthquake signatures is necessary for reliable forecasting. For this purpose, we investigate the similar and differing effects of magnetic storms and earthquakes on the ionospheric structure. In this paper, we mainly focused the time period after magnetic storms and before earthquakes. We select earthquakes occurred between 1998 and 2013 with M>6 and depth<30 km. We examined the temporal and spatial distribution of TEC using GIM-TEC data to detect the anomalous behavior and we found 28 earthquakes had anomalous changes. We further investigate their 3D distributions for these earthquakes with tomography and found 13 among them show the similar anomalous structure. Meanwhile, we select magnetic storms between 1998 and 2013 with Dst < -100 nT and the onset time from 6 am to 6 pm. Then, 42 magnetic storms were extracted. We analyzed arbitrarily 10 different storms and the same analyses were performed. We further employed the ionospheric foEs, NmF2 and hmF2 quantities as complementary data. Then, we prepared time series figures of these parameters and compared their responses against storm and earthquake effects. Results will be shown in the presentation.