2017
DOI: 10.1093/gji/ggx117
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Seismic evidence for broad attenuation anomalies in the asthenosphere beneath the Pacific Ocean

Abstract: We present QADR17, a global model of Rayleigh-wave attenuation based on a massive surface wave data set (372 629 frequency-dependent attenuation curves in the period range 50-260 s). We correct for focusing-defocusing effects and geometrical spreading, and perform a stringent selection to only keep robust observations. Then, data with close epicentres recorded at the same station are clustered, as they sample the same Earth's structure. After this preselection, our data set consists of about 35 000 curves that… Show more

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Cited by 11 publications
(20 citation statements)
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“…The initial data set consists of 372,629 epicenter station path‐average attenuation curves measured in the period range 40–240 s for the fundamental mode and up to the fifth overtone. From these observations, fundamental mode attenuation maps were obtained by Adenis et al []. We supplement these maps with higher‐mode attenuation maps based on Debayle and Ricard 's [] data set.…”
Section: Regionalizationmentioning
confidence: 99%
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“…The initial data set consists of 372,629 epicenter station path‐average attenuation curves measured in the period range 40–240 s for the fundamental mode and up to the fifth overtone. From these observations, fundamental mode attenuation maps were obtained by Adenis et al []. We supplement these maps with higher‐mode attenuation maps based on Debayle and Ricard 's [] data set.…”
Section: Regionalizationmentioning
confidence: 99%
“…Here we only summarize the main specificities of the regionalization which is described in details in Adenis et al []: A thorough data selection is applied to reject data likely to be biased by errors in the source or in the instrumental response. We use great circle ray theory and focusing effects are accounted for along each ray using the formalism of Woodhouse and Wong []. We discard paths longer than 110° where Woodhouse and Wong 's [] formalism overestimates the amplitudes [ Dalton et al , ]. Unlike earlier studies, we invert for ln(Q) rather than for Q or Q −1 . This brings the data close to a Gaussian distribution, avoids unphysical negative values, and allows for a much larger range of variations. All maps are obtained using a Gaussian correlation function having a standard deviation of 10° and an a priori error set to 20% of the a priori model.…”
Section: Regionalizationmentioning
confidence: 99%
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