Details

Autor(en) / Beteiligte

• Wang, Fengfan
• Yu, Jia
• Liu, Zhijie
• Kong, Min
• Wu, Yunfan

Titel

Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm

Ist Teil von

• Computers & geosciences, 2021-04, Vol.149, p.104713, Article 104713

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed sediment, a two-stage model was established to predict a sample's class using the XGBoost algorithm as well as the grain size parameters as input features. The final model was evaluated with quantitative performance measures of recall, precision and F1 score, and by comparing sediment texture maps using the predicted and the actual data. The results show that the model performs well on extraction of sediment samples without gravel fraction, and prediction of classes that have independent characteristics of grain size parameters or samples not near the boundaries of classes in the ternary diagram. The predicted sediment texture is close to the actual and could be reliable due to errors with little impact on further applications. It is demonstrated that the model could be an auxiliary or alternative approach to offshore sediment texture mapping, as well as supplementary to the analysis of sedimentary environment. •Apply XGBoost Algorithm to study the Folk's textual classification scheme.•Evaluate the uncertainty of prediction using confusion matrix.•Boundaries between sediment classes on the diagram are where the errors occur.•Error regions may indicate to complicated areas of seabed transport.•Classes of sand, sandy silt, silty sand and silt predicted by the model are reliable.