Articles | Volume 9, issue 5
Research article
03 Sep 2021
Research article |  | 03 Sep 2021

Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application

Hajime Naruse and Kento Nakao

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Cited articles

Allen, J. R. L.: Sedimentary structures: their character and physical basis, vol. 1, Elsevier Scientific Publishing Company, Amsterdam, 1982. a
Allen, J. R. L.: The Bouma division A and the possible duration of turbidity currents, J. Sediment. Res., 61, 291–295, 1991. a
Amy, L. A. and Talling, P. J.: Anatomy of turbidites and linked debrites based on long distance (120 × 30 km) bed correlation, Marnoso Arenacea Formation, Northern Apennines, Italy, Sedimentology, 53, 161–212,, 2006. a
Amy, L. A., Kneller, B., and McCaffrey, W.: Evaluating the links between turbidite characteristics and gross system architecture: upscaling insights from the turbidite sheet-system of Peïra Cava, SE France, in: Deep Water Reservoirs of the World, Gulf Coast Section SEPM Foundation 20th Annual Research Conference, 3–6 December 2000, SEPM, Houston, TX (CD-ROM), 1, p. 15, 2000. a
Amy, L. A., McCaffrey, W. D., and Kneller, B. C.: The influence of a lateral basin-slope on the depositional patterns of natural and experimental turbidity currents, Geological Society, London, Special Publications, 221, 311–330,, 2004. a
Short summary
This paper proposes a method to reconstruct the hydraulic conditions of turbidity currents from turbidites. We investigated the validity and problems of this method in application to actual field datasets using artificial data. Once this method is established, it is expected that the method will elucidate the generation process of turbidity currents and will help to predict the geometry of resultant turbidites in deep-sea environments.