Bayesian ISOLA: new tool for automated centroid moment tensor inversion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F17%3A00476624" target="_blank" >RIV/67985530:_____/17:00476624 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/17:10367209

Výsledek na webu

<a href="http://dx.doi.org/10.1093/gji/ggx158" target="_blank" >http://dx.doi.org/10.1093/gji/ggx158</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/gji/ggx158" target="_blank" >10.1093/gji/ggx158</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian ISOLA: new tool for automated centroid moment tensor inversion

Popis výsledku v původním jazyce

We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances are rejected and full-waveform inversion in a space-time grid around a provided hypocentre. A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequency ranges. The method is tested on synthetic and observed data. It is applied on a data set from the Swiss seismic network and the results are compared with the existing high-quality MT catalogue. The software package programmed in Python is designed to be as versatile as possible in order to be applicable in various networks ranging from local to regional. The method can be applied either to the everyday network data flow, or to process large pre-existing earthquake catalogues and data sets.

Název v anglickém jazyce

Bayesian ISOLA: new tool for automated centroid moment tensor inversion

Popis výsledku anglicky

We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances are rejected and full-waveform inversion in a space-time grid around a provided hypocentre. A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequency ranges. The method is tested on synthetic and observed data. It is applied on a data set from the Swiss seismic network and the results are compared with the existing high-quality MT catalogue. The software package programmed in Python is designed to be as versatile as possible in order to be applicable in various networks ranging from local to regional. The method can be applied either to the everyday network data flow, or to process large pre-existing earthquake catalogues and data sets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10507 - Volcanology

Projekt

<a href="/cs/project/GA14-04372S" target="_blank" >GA14-04372S: Složitost zdrojů tektonických zemětřesení na různých časoprostorových škálách</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Geophysical Journal International

ISSN

0956-540X

e-ISSN

—

Svazek periodika

210

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

693-705

Kód UT WoS článku

000409283300010

EID výsledku v databázi Scopus

2-s2.0-85029959939