2017
DOI: 10.1093/gji/ggx264
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Crustal surface wave velocity structure of the east Albany-Fraser Orogen, Western Australia, from ambient noise recordings

Abstract: S U M M A R YGroup and phase velocity maps in the period range 2-20 s for the Proterozoic east AlbanyFraser Orogen, Western Australia, are extracted from ambient seismic noise recorded with the 70-station ALFREX array. This 2 yr temporary installation provided detailed coverage across the orogen and the edge of the Neoarchean Yilgarn Craton, a region where no passive seismic studies of this scale have occurred to date. The surface wave velocities are rather high overall (>3 km s −1 nearly everywhere), as expec… Show more

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Cited by 6 publications
(7 citation statements)
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“…and a smaller low-velocity zone (L2) to the south (Figure 8a), consistent with the observations from the study by Sippl et al (2017). The inversion of synchronous C 2 functions largely confirms the velocity pattern observed in the C 1 result, with an average velocity difference of 0.02 km/s across the study region.…”
Section: Ambient Noise Tomography With Egfs From Srisupporting
confidence: 88%
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“…and a smaller low-velocity zone (L2) to the south (Figure 8a), consistent with the observations from the study by Sippl et al (2017). The inversion of synchronous C 2 functions largely confirms the velocity pattern observed in the C 1 result, with an average velocity difference of 0.02 km/s across the study region.…”
Section: Ambient Noise Tomography With Egfs From Srisupporting
confidence: 88%
“…the C 1 functions (Figure 6a). The range of group velocity measurements (i.e., standard deviations) are similar between the two sets of EGFs and is also consistent with the measurements from an earlier study (Sippl et al, 2017).…”
Section: Traveltime and Dispersion Measurementssupporting
confidence: 87%
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“…Another promising application of C 2 is its ability to tie temporary arrays deployed at different times (e.g., the campaign mode arrays in eastern Australia) to constrain the inter-array structure, which remains largely undersampled due to the current network topology. With much improved data sampling, the existing regional (e.g., Sippl et al 2017) or continental scale crustal models (e.g., Saygin and Kennett 2012) can be further refined and eventually lead to a better understanding of the Australian continent.…”
Section: Methods and Resultsmentioning
confidence: 99%