Bayesian Reassessment of Seismic Moment Tensors and Their Uncertainties in the Adriatic Sea Region

The determination of seismic moment tensor (MT) parameters is subject to uncertainties from data noise and structural error due to the imperfect Earth model, which is rarely considered in regional earthquake catalogs. In this study, we apply a hierarchical Bayesian MT inversion with uncertainty quantification to seven moderate-earthquakes Mw 4.5–5.5 in the Adriatic Sea region. The event collection includes three in mainland Croatia: the 2020 Mw 5.4 Zagreb earthquake and its Mw 4.9 aftershock, and the Mw 5.0 foreshock of the 2020 Petrinja earthquake, two events in the offshore Adriatic Sea: the 2021 Mw 5.2 central Adriatic earthquake, the 2024 Mw 4.6 southern Adriatic earthquake, and two in Italy: the 2022 Mw 5.5 Costa Marchigiana-Pesarese earthquake, and the 2023 Mw 4.9 earthquake in Marradi (Tuscany). The inversion output features the source depth and the posterior distributions of the MT parameters, enabling the uncertainty quantification. Comparing our results with regional routine catalogs highlights the improvement in source determination, particularly in confidence of non-double-couple components when incorporating the data and structural uncertainties. The refined source mechanisms could be useful for understanding the complex geological settings, assessing the hazard potential, and further improving the regional earthquake catalogs in the Adriatic Sea region.

Authors

Institutions

Publication Details

Journal
Seismica
Published
2025-11-25
DOI
https://doi.org/10.26443/seismica.v4i2.1721
Primary Topic
earthquake and tectonic studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bayesian Reassessment of Seismic Moment Tensors and Their Uncertainties in the Adriatic Sea Region

Thanh‐Son Phạm, ‪Hrvoje Tkalčić, Jinyin Hu, Iva Dasović et al.
Seismica
earthquake and tectonic studies
article

Bayesian Reassessment of Seismic Moment Tensors and Their Uncertainties in the Adriatic Sea Region

Thanh‐Son Phạm, ‪Hrvoje Tkalčić, Jinyin Hu, Iva Dasović, Marijan Herak, Marija Mustać
article en

Abstract

The determination of seismic moment tensor (MT) parameters is subject to uncertainties from data noise and structural error due to the imperfect Earth model, which is rarely considered in regional earthquake catalogs. In this study, we apply a hierarchical Bayesian MT inversion with uncertainty quantification to seven moderate-earthquakes Mw 4.5–5.5 in the Adriatic Sea region. The event collection includes three in mainland Croatia: the 2020 Mw 5.4 Zagreb earthquake and its Mw 4.9 aftershock, and the Mw 5.0 foreshock of the 2020 Petrinja earthquake, two events in the offshore Adriatic Sea: the 2021 Mw 5.2 central Adriatic earthquake, the 2024 Mw 4.6 southern Adriatic earthquake, and two in Italy: the 2022 Mw 5.5 Costa Marchigiana-Pesarese earthquake, and the 2023 Mw 4.9 earthquake in Marradi (Tuscany). The inversion output features the source depth and the posterior distributions of the MT parameters, enabling the uncertainty quantification. Comparing our results with regional routine catalogs highlights the improvement in source determination, particularly in confidence of non-double-couple components when incorporating the data and structural uncertainties. The refined source mechanisms could be useful for understanding the complex geological settings, assessing the hazard potential, and further improving the regional earthquake catalogs in the Adriatic Sea region.

SeismicaVol. 4(2)
Australian National University (AU), University of Zagreb (HR)
Openalex Percentile: Top 62%
earthquake and tectonic studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.