Application of the adjoint approach to optimise the initial conditions of a turbidity current with the AdjointTurbidity 1.0 model

Abstract

Abstract. Turbidity currents are one of the main drivers of sediment transport from the continental shelf to the deep ocean. The resulting sediment deposits can reach hundreds of kilometres into the ocean. Computer models that simulate turbidity currents and the resulting sediment deposit can help us to understand their general behaviour. However, in order to recreate real-world scenarios, the challenge is to find the turbidity current parameters that reproduce the observations of sediment deposits. This paper demonstrates a solution to the inverse sediment transportation problem: for a known sedimentary deposit, the developed model reconstructs details about the turbidity current that produced the deposit. The reconstruction is constrained here by a shallow water sediment-laden density current model, which is discretised by the finite-element method and an adaptive time-stepping scheme. The model is differentiated using the adjoint approach, and an efficient gradientbased optimisation method is applied to identify the turbidity parameters which minimise the misfit between the modelled and the observed field sediment deposits. The capabilities of this approach are demonstrated using measurements taken in the Miocene Marnoso-arenacea Formation (Italy). We find that whilst the model cannot match the deposit exactly due to limitations in the physical processes simulated, it provides valuable insights into the depositional processes and represents a significant advance in our toolset for interpreting turbidity current deposits.

Inverse Analysis of Experimental Scale Turbidity Currents Using Deep Learning Neural Networks

“…Since the parallelization of the forward model calculation significantly reduced the calculation time, a more accurate and realistic forward model with a heavier calculation load could be implemented. As a result, the forward model used in this research is much better at capturing the spatio-temporal evolution of turbidity current than the forward model used in previous research (Falcini et al, 2009;Parkinson et al, 2017). Falcini et al (2009) used a steady flow forward model, whereas our forward model is a nonsteady flow model that reproduces the evolution of flow over time.…”

“…However, each epoch of optimization requires the selection results from the previous epoch, and thus, the calculation of the forward model cannot be parallelized over epochs. In the adjoint method used by Parkinson et al (2017), control variables within the forward model of turbidity currents are first initialized and inputted into the numerical model. The turbidite deposit profile is calculated and compared with the target values using a cost function.…”

“…The results provided an estimation of the hydraulic conditions of flow at a single location, but did not provide a reconstruction of the spatial evolution of the turbidity current. In contrast, inverse analysis methods in previous studies based on numerical models provided more detailed insights into the spatial structure and evolution of flows over time (e.g., Falcini et al, 2009;Lesshafft et al, 2011;Parkinson et al, 2017). The method proposed by Falcini et al (2009) assumed steady flow conditions and was simplified for obtaining analytical solutions, preventing it from accurately illustrating the flow mechanism of unsteady turbidity currents that can produce normally graded bedding.…”

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

“…Thus, if the computational load of the forward model is high, the inverse analysis takes an unrealistic amount of time. Parkinson et al (2017) applied the adjoint method with the gradient-based optimization algorithm.…”

“…Conversely, Lesshafft et al (2011) applied a direct numerical simulation model for the inversion of turbidite, whose application however to field-scale data is difficult because of the high calculation cost. Parkinson et al (2017) proposed a method applicable to non-steady field scale flows by using a layer-average model as the forward model, which is potentially applicable to turbidites in outcrops. However, the flow conditions predicted from ancient turbidites were quite unrealistic in their study.…”