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Journal Articles Geophysical Journal International Year : 2020

Moho depth of the British Isles: a probabilistic perspective

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Abstract

We present a new Moho depth model of the British Isles and surrounding areas from the most up-to-date compilation of Moho depth estimates obtained from refraction, reflection and receiver function data. We use a probabilistic, trans-dimensional and hierarchical approach for the surface reconstruction of Moho topography. This fully data-driven approach allows for adaptive parametrization, assessment of relative importance between different data-types and uncertainties quantification on the reconstructed surface. Our results confirm the first order features of the Moho topography obtained in previous work such as deeper Moho (29-36 km) in continental areas (e.g. Ireland and Great Britain) and shallower Moho (12-22 km) offshore (e.g in the Atlantic Ocean, west of Ireland). Resolution is improved by including recent available data, especially around the Porcupine Basin, onshore Ireland and Great Britain. NE trending features in Moho topography are highlighted above the Rockall High (about 28 km) and the Rockall Trough (with a NE directed deepening from 12 to about 20 km). A perpendicular SE oriented feature (Moho depth 26-28 km) is located between the Orkney and the Shetland, extending further SW in the North Sea. Onshore, our results highlight the crustal thinning toward the N in Ireland and an E-W oriented transition between deep (34 km) and shallow (about 28 km) Moho in Scotland. Our probabilistic results are compared with previous models showing overall differences around ±2 km, within the posterior uncertainties calculated with our approach. Bigger differences are located where different data are used between models or in less constrained areas where posterior uncertainties are high.
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insu-02506722 , version 1 (12-03-2020)

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Andrea Licciardi, R W England, N Piana Agostinetti, Kerry Gallagher. Moho depth of the British Isles: a probabilistic perspective. Geophysical Journal International, 2020, 221 (2), pp.1384-1401. ⟨10.1093/gji/ggaa021⟩. ⟨insu-02506722⟩
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