An automatized XKS-splitting procedure for large data sets: Extension package for SplitRacer and application to the USArray

  • Highlights • Full automatized analysis of teleseismic XKS shear wave splitting. • Rapid analysis of large seismological data sets. • Automated window selection and quality classification. • Application to the USArray Transportable Array including expansion to Alaska. • Improved statistical evidence and objectivity of derived effective splitting. Abstract Recent technological advances have led to community wide use of large-scale seismic experiments which produce seismic data on previously impossible scales. Standard processing procedures thus require automatization to facilitate a fast and objective analysis of the data. Among these, XKS-splitting is an important tool to derive first insights into the Earth's deformation regimes at depth by studying seismic anisotropy. Most often, shear-wave splitting is interpreted to represent crystallographic preferred orientation (CPO) of mantle minerals like olivine as dominating feature and can thus be used as a proxy of mantle flow processes. Here, we introduce an addition to the MATLAB®-based SplitRacer tool box (Reiss and Rümpker 2017) which automatizes the entire XKS-splitting procedure. This is achieved by the automatization of 1) choosing a time window based on spectral analyses and 2) categorization of results based on three different XKS-splitting methods (energy minimization, rotation correlation and splitting intensity). This provides effective and objective results for splitting as well as null-measurement results. This extension allows to use SplitRacer without a graphical interface and introduces a bootstrapping statistics as error estimate of the single layer joint splitting method. The procedures are designed to allow a fast and more objective analysis of a vast amount of data, as produced by recent seismic deployments (e.g. USArray, AlpArray). We test this automatization by applying the analysis to the USArray data set, which has approximately 1900 stations with between two to fifteen years of data. We can reproduce the general pattern of the results from former studies with the more objective automatic analysis. Based on a joint-splitting approach, we approximate the splitting effect at individual stations by a single anisotropic layer. As we include null-measurements as well as a larger data set as previous studies, we can provide improved statistical evidence for these effective splitting parameters.

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Metadaten
Author:Frederik LinkORCiDGND, Miriam Christina ReissORCiDGND, Georg RümpkerORCiD
URN:urn:nbn:de:hebis:30:3-782685
DOI:https://doi.org/10.1016/j.cageo.2021.104961
ISSN:0098-3004
Parent Title (English):Computers & geosciences
Publisher:Elsevier
Place of publication:Amsterdam [u.a.]
Document Type:Article
Language:English
Date of Publication (online):2021/11/05
Date of first Publication:2021/10/23
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2024/03/20
Tag:Alaska; Anisotropy; Automatization; Mantle flow; Shear-wave splitting; USArray transportable array
Volume:158
Issue:104961
Article Number:104961
Page Number:11
HeBIS-PPN:517909944
Institutes:Geowissenschaften / Geographie / Geowissenschaften
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0