Geoacoustic and geophysical data-driven seafloor sediment classification through machine learning algorithms with property-centered oversampling techniques

This study aims to classify seafloor sediments using physics-inspired and data-driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S- and P-wave velocities and depth. The soil information reported in the original literature and the "six reference sediments" and effective stress-versus-depth models proposed by the previous study confirm the sediment type across all of the input variables. We use three machine learning algorithms and four oversampling methods to enhance the performance accuracy and overcome data imbalance in the minority class. The results show that the averaged accuracy of sediment classification with original data corresponds to 0.88 for porosity, 0.61 for S-wave velocity, and 0.97 for P-wave velocity. In particular, the enhanced accuracy with oversampled input variables becomes more pronounced when the depth data are considered in a dataset. The class-oriented grouping method newly proposed in this study appears to be a robust approach to enhancing performance. Surprisingly, model-based input variables lead to the best performance in all cases. The proposed analyses conducted using machine learning algorithms and oversampling techniques within the physics-inspired models could be extended to obtain a first-order assessment of marine sediment properties.