The sufficiency of accurate data is a core element in data-centric geotechnics. However, geotechnical datasets are essentially uncertain, whereupon engineers have difficulty with obtaining precise information for making decisions. This challenge is more apparent when the performance of data-driven technologies solely relies on imperfect databases or even when it is sometimes difficult to investigate sites physically. This paper introduces geotechnical property estimation from noisy and incomplete data within the labeled random finite set (LRFS) framework. We leverage the ability of the generalized labeled multi-Bernoulli (GLMB) filter, a fundamental solution for multi-object estimation, to deal with measurement uncertainties from a Bayesian perspective. In particular, this work focuses on the similarity between LRFSs and big indirect data (BID) in geotechnics as those characteristics resemble each other, which enables GLMB filtering to be exploited potentially for data-centric geotechnical engineering. Experiments for numerical study are conducted to evaluate the proposed method using a public clay database.

翻译：在地质工程的数据中心化研究中，准确数据的充分性是核心要素。然而，地质数据集本质上具有不确定性，工程师往往难以获得精确信息以支持决策。当数据驱动技术的性能完全依赖于不完善的数据库，甚至有时难以实地勘察场地时，这一挑战更为突出。本文在标记随机有限集框架下，提出了从含噪声和不完整数据中估计地质属性的方法。我们利用广义标记多伯努利滤波器——一种多目标估计的基础解法——的能力，从贝叶斯角度处理测量不确定性。本研究特别关注标记随机有限集与地质工程中的大规模间接数据之间的相似性，这些特征的高度相似性使得GLMB滤波技术有望应用于数据中心化的地质工程实践。通过数值实验，我们使用公开的黏土数据库对所提方法进行了评估。