Seismological Processing of Six Degree-of-Freedom Ground-Motion Data.
gyroscope
ring laser
rotation
seismic exploration
seismic tomography
seismology
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
03 Dec 2020
03 Dec 2020
Historique:
received:
05
10
2020
revised:
27
11
2020
accepted:
01
12
2020
entrez:
8
12
2020
pubmed:
9
12
2020
medline:
9
12
2020
Statut:
epublish
Résumé
Recent progress in rotational sensor technology has made it possible to directly measure rotational ground-motion induced by seismic waves. When combined with conventional inertial seismometer recordings, the new sensors allow one to locally observe six degrees of freedom (6DOF) of ground-motion, composed of three orthogonal components of translational motion and three orthogonal components of rotational motion. The applications of such 6DOF measurements are manifold-ranging from wavefield characterization, separation, and reconstruction to the reduction of non-uniqueness in seismic inverse problems-and have the potential to revolutionize the way seismic data are acquired and processed. However, the seismological community has yet to embrace rotational ground-motion as a new observable. The aim of this paper is to give a high-level introduction into the field of 6DOF seismology using illustrative examples and to summarize recent progress made in this relatively young field. It is intended for readers with a general background in seismology. In order to illustrate the seismological value of rotational ground-motion data, we provide the first-ever 6DOF processing example of a teleseismic earthquake recorded on a multicomponent ring laser observatory and demonstrate how wave parameters (phase velocity, propagation direction, and ellipticity angle) and wave types of multiple phases can be automatically estimated using single-station 6DOF processing tools. Python codes to reproduce this processing example are provided in an accompanying Jupyter notebook.
Identifiants
pubmed: 33287180
pii: s20236904
doi: 10.3390/s20236904
pmc: PMC7731287
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Horizon 2020
ID : 821881
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