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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The Korea Meteorological Administration (KMA) provides detailed hypocenter information after the earthquake early warning (EEW) service, due to increased public interest and for the study of fault movements. However, the rapid production of hypocenter information has limitations, including the necessity for the calculation of focal mechanisms, which requires expertise in seismology. Therefore, we developed automatic focal mechanisms (AFMs) based on the time domain moment tensor inversion method. A key feature of AFMs is the automatic collection and reforming of waveform data using information for EEW. Furthermore, we propose an additional module of the iterative inversion by reducing the low variance reduction data. This shows the increased variance reduction value rather than that of the first inversion. The variance reductions for the first inversion results were between 59 and 94%, whilst the results of the second inversion using the additional module were increased to 79–97%. The accuracy of the automatic results was similar to that of the manually determined results and was well adapted to the local earthquakes in and around the Korean Peninsula. The KMA provided the focal mechanisms of local earthquakes that could then be automatically determined using the EEW information within approximately 6–8 min and subsequently reported.

Details

Title
Automatic Fault Plane Solution for the Provision of Rapid Earthquake Information in South Korea
Author
Lee, Jimin  VIAFID ORCID Logo  ; Lee, Duk Kee; Ahn, Jae-Kwang  VIAFID ORCID Logo 
First page
520
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20711050
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2761217064
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.