用便携式探针数据+机器学习，91%准确率自动分类海底沉积物类型
Probabilistic Classification of Near-Surface Shallow-Water Sediments using A Portable Free-Fall Penetrometer
- 基于便携式自由落体探针数据，用机器学习建立沉积物分类模型
- 在多个地点测试中达到91.1%分类准确率，覆盖四类沉积物
- 不仅能预测类别，还能量化预测不确定性，适合工程评估参考
海底沉积物的地质力学评估对工程与海军应用至关重要，能提供沉积物性质、行为和强度的重要信息。获取高质量海底样本常具挑战性，因此原位测试成为场地表征的关键。自由落体探针（FFP）是快速剖面化近岸或河口浅水区沉积物的有效工具，适用于浅层及深层环境。尽管传统海上锥贯入测试（CPT）数据解析方法成熟，但其向FFP数据的迁移仍处于研究阶段。本研究提出一种创新方法，利用机器学习算法，基于多个地点（如华盛顿州塞昆姆湾、波托马克河、弗吉尼亚州约克河）的便携式自由落体探针（PFFP）数据，构建沉积物行为分类系统。结果显示，该模型在四类沉积物分类上达到91.1%准确率：无黏性且几乎无塑性（第1类）、无黏性且有一定塑性（第2类）、低塑性黏性（第3类）、高塑性黏性（第4类）。模型不仅输出分类结果，还提供预测内在不确定性估计，该不确定性随沉积物组成、环境条件和操作技术变化而显著波动。通过量化不确定性，模型为沉积物分类提供了更全面、更可靠的决策支持。
原文摘要 · Abstract (English)
The geotechnical evaluation of seabed sediments is important for engineering projects and naval applications, offering valuable insights into sediment properties, behavior, and strength. Obtaining high-quality seabed samples can be a challenging task, making in situ testing an essential part of site characterization. Free-fall penetrometers (FFPs) are robust tools for rapidly profiling seabed surface sediments, even in energetic nearshore or estuarine conditions and shallow as well as deep depths. Although methods for interpretation of traditional offshore cone penetration testing (CPT) data are well-established, their adaptation to FFP data is still an area of research. This study introduces an innovative approach that utilizes machine learning algorithms to create a sediment behavior classification system based on portable free- fall penetrometer (PFFP) data. The proposed model leverages PFFP measurements obtained from multiple locations, such as Sequim Bay (Washington), the Potomac River, and the York River (Virginia). The results show 91.1% accuracy in the class prediction, with the classes representing cohesionless sediment with little to no plasticity (Class 1), cohesionless sediment with some plasticity (Class 2), cohesive sediment with low plasticity (Class 3), and cohesive sediment with high plasticity (Class 4). The model prediction not only predicts classes but also yields an estimate of inherent uncertainty associated with the prediction, which can provide valuable insight into different sediment behaviors. Lower uncertainties are more common, but they can increase significantly depending on variations in sediment composition, environmental conditions, and operational techniques. By quantifying uncertainty, the model offers a more comprehensive and informed approach to sediment classification
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