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.

民间的文本分类方案被广泛用于沉积物研究，通常按砾石，沙子，粉砂和粘土部分的比例进行操作。但是，处理来自不同来源的数据通常需要面对缺失值，这可能会使分类变得困难。为了解决该问题并发现其他分析方案的方法，以近海海底沉积物样本为基础，建立了一个两阶段模型，使用XGBoost算法以及粒度参数作为输入特征来预测样本类别。使用召回率，精度和F1分数的定量性能指标，并使用预测数据和实际数据比较沉积物纹理图，对最终模型进行评估。结果表明，该模型在没有砾石组分的沉积物样品提取中表现良好，并预测具有晶粒尺寸参数或样本的独立特征的三元图中不接近类别边界的类别。预测的沉积物质地接近实际，并且由于误差而对其他应用几乎没有影响，因此可能是可靠的。结果表明，该模型可以作为海上沉积物纹理制图的辅助或替代方法，也可以作为沉积环境分析的补充。