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

Abstract Turbidites have been widely studied as indicators of the occurrences and magnitudes of paleo‐tsunamis and paleo‐earthquakes. Inversion to estimate 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 and evaluated its performance using synthetic and flume experiment data sets. The model successfully estimated model input parameters with a symmetric mean absolute percentage error (SMAPE) of less than 29.7%, except for the density‐equivalent sediment concentration for saline water at the inlet. When applied to experimental data, the model reasonably reconstructed flow conditions, yielding SMAPE values between 51.7% and 86.2%, despite potential uncertainties introduced by sampling disturbances, data processing, and forward model limitations. These values were reasonable even compared to the results of the previous one‐dimensional inverse model (Cai & Naruse, 2021, https://doi.org/10.1029/2021jf006276 ), which showed a SMAPE range of 24.2%–113%. The spatial distribution of bed thickness was also well predicted, except when most of the suspension bypassed the depositional zone. Overall, the proposed inverse models demonstrated accuracy comparable to that of the previous one‐dimensional model. They offer greater applicability to complex seafloor geometries and maintain low computational costs, providing a practical advantage over optimization‐based methods. These results suggest that the proposed method is well‐suited for the field‐scale inversion of turbidity currents in realistic geological settings.