Experimental Verification of a Two-Dimensional Inverse Method for Turbidity Currents Using a Deep Neural Network

Turbidites have been widely studied as indicators of the occurrences and magnitudes of paleo-tsunamis and paleo-earthquakes. Inversion to estimate the flow conditions from turbidites offers valuable insights into the magnitudes of paleo-seismic and tsunami events. However, conventional one-dimensional inverse models are insufficient for capturing the behavior of turbidity currents in tectonically active margins, where the seafloor topography is typically complex. Here, we developed a horizontal two-dimensional inverse model of turbidity currents based on a deep neural network (DNN) and evaluated its performance using both synthetic and flume experiment datasets. The model successfully estimated the model input parameters with a symmetric mean absolute percentage error (SMAPE) of less than 32.5%, except for the density-equivalent sediment concentration for saline water at the inlet. When applied to experimental data, the model reasonably reconstructed the flow conditions, yielding SMAPE values between 51.7 and 86.2%, despite the potential uncertainties introduced by sampling disturbances, data processing, and forward model limitations. The spatial distribution of bed thickness was also well predicted, except in cases where most of the suspension bypassed the depositional zone. Overall, the proposed inverse model demonstrated accuracy comparable to the previous one-dimensional model while offering greater applicability to complex seafloor geometries and maintaining low computational costs. These results suggest that the proposed method is well-suited for the field-scale inversion of turbidity currents in realistic geological settings.