Despite the importance of turbidity currents in environmental and resource geology, their flow conditions and mechanism are not well understood. To resolve this issue, a new method for the inverse analysis of turbidity current using deep learning neural network (DNN) was proposed. This research aims at verifying and calibrating this method using artificial and flume experiment datasets. The forward model based on the shallow water equation was employed in this study to produce artificial datasets of turbidite deposits. DNN was applied to two hundred artificial datasets and two sets of experiment data. As a result of inversion by DNN, spatial distributions of grain size and thickness of experimental turbidites were reconstructed accurately. With regard to hydraulic conditions, the flow heights were reasonably estimated, and sediment concentrations reconstructed were reasonable except for regions where the values measured in experiments were extremely low. Flow duration also showed reasonable reconstructed values. In contrast to the other values, there was a large discrepancy between the measured and reconstructed values of flow velocity. The reason for this discrepancy in velocity may be attributed to inaccuracy in closure functions employed in the forward model. Future application to actual data of natural scale turbidite is anticipated.

Despite the importance of turbidity currents in environmental and resource geology, their flow conditions and mechanism are not well understood. To resolve this issue, a novel method for the inverse analysis of turbidity current using deep learning neural network (DNN) was proposed. This study aims to verify this method using artificial and flume experiment datasets. Development of inverse model by DNN involves two steps. First, artificial datasets of turbidites are produced using a forward model based on shallow water equation. To develop a inverse model, DNN then explores the functional relationship between initial flow conditions and characteristics of the turbidite deposit through the processing of artificial datasets. The developed inverse model was applied to 200 sets of artificial test data and four sets of experiment data. Results of inverse analysis of artificial test data indicated that the flow conditions can be precisely reconstructed from depositional characteristics of turbidites. For experimental turbidites, spatial distributions of grain size and thickness were accurately reconstructed. With regard to hydraulic conditions, reconstructed values of flow heights, sediment concentrations, and flow durations were close to the measured values. In contrast to the other values, there was a larger discrepancy between the measured and reconstructed values of flow velocity, which may be attributed to inaccuracies in sediment entrainment functions employed in the forward model.

Despite the importance of turbidity currents in environmental and resource geology, their flow conditions and mechanisms are not well understood. This study proposes a novel method for the inverse analysis of turbidity currents using a deep learning neural network (DNN) to better explore the properties of turbidity currents. The aim of this study is to verify the DNN inverse method using numerical and flume experiment datasets. Numerical datasets of turbidites were generated with a forward model. Then, the DNN model was trained to find the functional relationship between flow conditions and turbidites by processing the numerical datasets. The performance of the trained DNN model was evaluated with 2000 numerical test datasets and 5 experiment datasets. Inverse analysis results on numerical test datasets indicated that flow conditions can be reconstructed from depositional characteristics of turbidites. For experimental turbidites, spatial distributions of grain size and thickness were consistent with the sample values. Concerning hydraulic conditions, flow depth H, layer-averaged velocity U, and flow duration Td were reconstructed with a certain level of deviation. Greater discrepancies between the measured and reconstructed values of flow concentration were observed relative to the former three parameters (H, U, Td), which may be attributed to difficulties in measuring the flow concentration during experiments. The precision of the reconstructions for experimental datasets was estimated using Jackknife resampling. Although the DNN model did not provide perfect reconstruction, it proved to be a significant advance for the inverse analysis of turbidity currents.