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 theMoho topography obtained in previouswork such as deeper Moho (29-36 km) in continental areas (e.g. Ireland and Great Britain) and shallowerMoho (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 towards 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.

Licciardi, A., England, R., Piana Agostinetti, N., Gallagher, K. (2020). Moho depth of the British Isles: A probabilistic perspective. GEOPHYSICAL JOURNAL INTERNATIONAL, 221(2), 1384-1401 [10.1093/gji/ggaa021].

Moho depth of the British Isles: A probabilistic perspective

Piana Agostinetti N.;
2020

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 theMoho topography obtained in previouswork such as deeper Moho (29-36 km) in continental areas (e.g. Ireland and Great Britain) and shallowerMoho (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 towards 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.
Articolo in rivista - Articolo scientifico
Crustal imaging; Crustal structure; Spatial analysis; Statistical methods;
English
24-feb-2020
2020
221
2
1384
1401
reserved
Licciardi, A., England, R., Piana Agostinetti, N., Gallagher, K. (2020). Moho depth of the British Isles: A probabilistic perspective. GEOPHYSICAL JOURNAL INTERNATIONAL, 221(2), 1384-1401 [10.1093/gji/ggaa021].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/302442
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