Inverse analysis to reconstruct hydraulic conditions of non-steady turbidity currents based on multiple grain-size classes

Abstract

This study proposes a new method of inverse analysis of ancient turbidites to represent non-steady turbidity currents and account for multiple grain-size classes. The forward model employed in this study is based on the shallow water equation, and the initial conditions of flows are assumed as a lock-exchange type. To obtain a solution to the inverse problem, a genetic algorithm is employed to determine the optimal initial conditions. The present method successfully estimated the true initial conditions of the turbidity currents from the artificial data sets of deposits created by the forward model. The method is also applied to a turbidite bed of the Kiyosumi Formation. The results of the inverse analysis yield solutions that fits well with the observed data of the individual turbidite, and provide estimates of the flow velocity, flow thickness and sediment concentration of the turbidity current. The flow thickness and velocity when the turbidity current reached the downstream end of the study area were reconstructed to be 334.6 m, 0.98 m/s, respectively.

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

“…Thus, the entire optimization procedure is difficult to parallelize. For instance, the kriging-based surrogate management method (Lesshafft et al, 2011) or the genetic algorithm (Nakao and Naruse, 2017) have been used to optimize the objective function for inversion of turbidity currents. In these methods, multiple calculations are conducted in each calculation step (generation), and the distribution of the objective function in the parametric space is iteratively estimated.…”

“…These extremely large or small estimates may be due to oversimplification in their forward model or failure in the optimization of the input parameters. Nakao and Naruse (2017) were the first to successfully perform an inverse analysis of turbidites using a general non-steady shallow water equation model. Although their reconstruction of the hydraulic conditions of the turbidity current was reasonable, the computational load of the inverse analysis was high because they used a genetic algorithm for optimization.…”