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

Year
2020
Type(s)
, ,
Author(s)
Stephen Arrowsmith; Junghyun Park; Il‐Young Che; Brian Stump; Gil Averbuch
Source
Seismological Research Letters, 2020
Url(s)
https://doi.org/10.1785/0220200292
BibTeX
BibTeX

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).

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