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A Deep Learning Approach to Quantitatively Characterising the Marine Near-SurfaceGold Open Access

Authors: M. Vardy and T. Darnell
Event name: 81st EAGE Conference and Exhibition 2019 Workshop Programme
Session: WS02 Seismic Inversion for Marine Overburden Characterization
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901929
Organisations: EAGE
Language: English
Info: Abstract, PDF ( 185.98Kb )

Summary:
In this paper, we present a Deep Learning workflow developed specifically for inverting marine site investigation data, comparing and contrasting it against the results obtained using traditional stochastic inversion algorithms with both synthetic and field data. In particular, we assess its potential for rapidly deriving a range of subsurface parameterisations, including geotechnical engineering properties of direct interest for various site investigation problems.

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