Event Location with Sparse Data: When Probabilistic Global Search is Important

Autor:	Stephen J. Arrowsmith, Brian W. Stump, Gil Averbuch, Il-Young Che, Junghyun Park
Rok vydání:	2020
Předmět:	Geophysics 010504 meteorology & atmospheric sciences Event (relativity) Probabilistic logic Data mining 010502 geochemistry & geophysics computer.software_genre 01 natural sciences computer Geology 0105 earth and related environmental sciences Sparse matrix
Zdroj:	Seismological Research Letters. 92:976-985
ISSN:	1938-2057 0895-0695
DOI:	10.1785/0220200292
Popis:	Locating events with sparse observations is a challenge for which conventional seismic location techniques are not well suited. In particular, Geiger’s method and its variants do not properly capture the full uncertainty in model parameter estimates, which is characterized by the probability density function (PDF). For sparse observations, we show that this PDF can deviate significantly from the ellipsoidal form assumed in conventional methods. Furthermore, we show how combining arrival time and direction-of-arrival constraints—as can be measured by three-component polarization or array methods—can significantly improve the precision, and in some cases reduce bias, in location solutions. This article explores these issues using various types of synthetic and real data (including single-component seismic, three-component seismic, and infrasound).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::683bbed868cc9475ba113c04894f8be7 https://doi.org/10.1785/0220200292 Zobrazit plný text záznamu