Application of deep neural network combined with dynamic poroelasticity to define seismic velocities and porosity from cone penetrometer data
Application of deep neural network combined with dynamic poroelasticity to define seismic velocities and porosity from cone penetrometer data
Offshore wind plays a pivotal role in enhancing Europe's energy security and achieving energy decarbonization goals. However, expediting offshore wind deployment necessitates efficient and economical site investigation surveys. To address this challenge, we introduce a novel approach utilising a deep neural network (DNN) to establish correlations between geotechnical cone penetrometer test (CPT) data and shear wave velocity (
Marin Moreno, Hector
e466cafd-bd5c-47a1-8522-e6938e7086a4
Gourvenec, Susan
6ff91ad8-1a91-42fe-a3f4-1b5d6f5ce0b8
Charles, Jared
ff218ed7-09b0-4a1d-87d2-a54d8fbd1a3f
7 July 2024
Marin Moreno, Hector
e466cafd-bd5c-47a1-8522-e6938e7086a4
Gourvenec, Susan
6ff91ad8-1a91-42fe-a3f4-1b5d6f5ce0b8
Charles, Jared
ff218ed7-09b0-4a1d-87d2-a54d8fbd1a3f
Marin Moreno, Hector, Gourvenec, Susan and Charles, Jared
(2024)
Application of deep neural network combined with dynamic poroelasticity to define seismic velocities and porosity from cone penetrometer data.
In Proceedings of the 7th International Conference on Geotechnical and Geophysical Site Characterization.
8 pp
.
(doi:10.23967/isc.2024.248).
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Conference or Workshop Item
(Paper)
Abstract
Offshore wind plays a pivotal role in enhancing Europe's energy security and achieving energy decarbonization goals. However, expediting offshore wind deployment necessitates efficient and economical site investigation surveys. To address this challenge, we introduce a novel approach utilising a deep neural network (DNN) to establish correlations between geotechnical cone penetrometer test (CPT) data and shear wave velocity (
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Published date: 7 July 2024
Venue - Dates:
7th International Conference on Geotechnical and Geophysical Site Characterization, , Barcelona, Spain, 2024-06-18 - 2024-06-21
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Local EPrints ID: 491727
URI: http://eprints.soton.ac.uk/id/eprint/491727
PURE UUID: fe0477d2-3f1b-485a-927c-86c25b732f2b
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Date deposited: 03 Jul 2024 16:53
Last modified: 12 Jul 2024 02:13
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Author:
Hector Marin Moreno
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