Quantifying strong seismic propagation effects in the upper volcanic edifice using sensitivity kernels
Language
English
Obiettivo Specifico
4V. Processi pre-eruttivi
Status
Published
JCR Journal
JCR Journal
Issue/vol(year)
/554 (2021)
ISSN
0012-821X
Publisher
Elsevier
Pages (printed)
116683
Date Issued
2021
Abstract
In volcanic environments, the correct interpretation of the signals recorded by a seismic station is critical for a determination of the internal state of the volcano. Those signals contain information about both the seismic source and the properties of the path travelled by the seismic wave. Therefore, understanding the path effect is necessary for both source inversions and geophysical investigation of the volcanoes' properties at depth. We present an application of the seismic adjoint methodology and sensitivity kernel analysis to investigate seismic wave propagation effects in the upper volcanic edifice. We do this by performing systematic numerical simulations to calculate synthetic seismograms in two-dimensional models of Mount Etna, Italy, considering different wave velocity properties. We investigate the relationship between different portions of a seismogram and different parts of the structural volcano model. In particular, we examine the influence of known near-surface low-velocity volcanic structure on the recorded seismic signals. Results improve our ability to understand path effects highlighting the importance of the shallowest velocity structure in shaping the recorded seismograms and support recent studies that show that, although long-period seismic events are commonly associated with magma movements in resonant conduits, these events can be reproduced without the presence of fluids. We conclude that edifice heterogeneities impart key signatures on volcano seismic traces that must be considered when investigating volcano seismic sources.
Type
article
File(s)![Thumbnail Image]()
Loading...
Name
1-s2.0-S0012821X20306270-main.pdf
Description
Open Access published article
Size
4.41 MB
Format
Adobe PDF
Checksum (MD5)
4c78236bd8e146232cc50f51a8c9c4b0
