Anthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and approximations are present, as in any other modeling activity of real-world phenomena. This study aims at combining data assimilation techniques with a physically-based numerical model of anthropogenic land subsidence in a novel and comprehensive workflow, to overcome the main limitations concerning the way traditional deterministic analyses use the available measurements. The proposed methodology allows to reduce uncertainties affecting the model, identify the most appropriate rock constitutive behavior and characterize the most significant governing geomechanical parameters. The proposed methodological approach has been applied in a synthetic test case representative of the Upper Adriatic basin, Italy. The integration of data assimilation techniques into geomechanical modeling appears to be a useful and effective tool for a more reliable study of anthropogenic land subsidence.

人为地面沉降可以通过数值模型进行评估和预测，这些模型通常建立在确定性分析之上。但是，存在不确定性和近似值，就像在现实世界现象的任何其他建模活动中一样。本研究旨在将数据同化技术与基于物理的人为地面沉降数值模型相结合，采用一种新颖而全面的工作流程，以克服传统确定性分析使用可用测量方法的主要局限性。所提出的方法可以减少影响模型的不确定性，确定最合适的岩石本构行为并表征最重要的控制地质力学参数。所提出的方法论方法已应用于代表意大利上亚得里亚海盆地的综合测试案例。将数据同化技术整合到地质力学模型中似乎是一种有用且有效的工具，可以更可靠地研究人为地面沉降。