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

Naruse, Hajime; Nakao, Kento

doi:https://doi.org/10.5194/esurf-9-1091-2021

Articles | Volume 9, issue 5

https://doi.org/10.5194/esurf-9-1091-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esurf-9-1091-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Data sets

Training and test data set of thickness distribution of turbidites Hajime Naruse https://doi.org/10.5281/zenodo.5167615

Model code and software

nninv1d Hajime Naruse https://doi.org/10.5281/zenodo.5167581

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.