Monitoring the effect of the 2023 Kahramanmaraş earthquakes (Mw 7.7 and 7.6) on soil dominant frequency values: Insights from Eastern and Southeastern Anatolia, Türkiye
Author:
- 26 March 2026
- journal article
- Published by Gumushane University Journal of Science and Technology Institute in Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi
- Vol. 16 (2) , 432-445
Abstract
This study was conducted to understand whether mega earthquakes cause changes in soil dominant frequencies (f) and soil amplification factor (H/V) values. Based on this motivation, the Kahramanmaraş earthquakes (Mw 7.7 and 7.6) that occurred on February 6, 2023, were investigated for the f and H/V values beneath seismic stations located in the eastern and southeastern Anatolia regions before and after this mega earthquake. In this context, data from 43 seismic stations were used to investigate the situation pre- and post-earthquake periods using the Nakamura technique. Among the 43 seismic stations analyzed, significant changes were determined in the f and H/V values only at the Malatya-Battalgazi (MAYA) and Adıyaman-Kahta (NARI) seismic stations. At the MAYA station, the f values before the mega earthquakes were ~10 Hz, while the f values after the earthquake dropped to ~7 Hz. At the NARI station, the average f value, which was 2.5 Hz, rose to 6.3 in February. In terms of H/V ratio values, the MAYA station increased sharply in February, reaching approximately 12. The H/V values at the NARI station, which normally range between 1.0 and 2.5, reached 5.5 in February. At the MAYA station, the f and H/V values, which were high before the earthquake, underwent a character change in February and continued with lower values. At the NARI station, the f values increased significantly in February and March compared to other months. A similar pattern is observed in H/V values, with a particular increase in February. The findings indicate that in some cases, mega earthquakes can cause changes in soil characteristics. In the future, continuous autonomous monitoring of seismic station data using the Nakamura method with deep learning-based systems may provide a new perspective in terms of pre-earthquake warning.Keywords
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