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Seismic prediction of geological structures ahead of the tunnel using tunnel surface waves

机译：隧道面波对隧道前方地质构造的地震预测

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摘要

In order to keep up with the economic and safety demands of modern tunnel construction projects, especially in urban areas, there is a need to detect safety threats in real time while the construction is advancing. Tunnel prediction methods accompanying the drilling process can help to correlate and update a priori information on expected geological structures with their actual spatial location or even existence ahead of the tunnel face. We recently presented a seismic prediction approach using tunnel surface waves, which has already proven its potential during field surveys. However, common tunnel seismic data interpretation, regardless of the prediction method, requires human interaction. Either a specially trained field technician have to be present at the construction site or the data has to be uploaded to an office for further interpretation. In this work we present a simple but stable approach to automatically detect major geological structures ahead of the tunnel face. We focus on the accurate determination of the distance of fault zones or lithological boundaries from the tunnel face without any a priori information. By 3D seismic finite difference modelling we simulated a synthetic tunnel seismic survey that includes typical features encountered in tunnel construction. The developed prediction sequence was tested on these data and later successfully applied to two different tunnel data sets acquired at the Gotthard Base Tunnel (Switzerland) and during the construction of the 'Neuer Schluchterner Tunnel' close to Fulda (Germany).
机译：为了跟上现代隧道建设项目的经济和安全要求，特别是在城市地区，需要在建设进行中实时检测安全威胁。随钻过程的隧道预测方法可以帮助关联和更新有关预期地质结构的先验信息及其实际空间位置，甚至存在于隧道工作面之前。我们最近提出了一种使用隧道表面波的地震预测方法，该方法已经在现场调查中证明了其潜力。但是，无论采用哪种预测方法，普通隧道地震数据的解释都需要人与人的互动。要么必须在现场训练有素的现场技术人员，要么必须将数据上传到办公室进行进一步解释。在这项工作中，我们提出了一种简单而稳定的方法来自动检测隧道工作面之前的主要地质结构。我们专注于在没有任何先验信息的情况下准确确定断层带或岩性边界距隧道工作面的距离。通过3D地震有限差分建模，我们模拟了包括隧道施工中遇到的典型特征的综合隧道地震勘测。在这些数据上测试了开发的预测序列，随后成功地将其应用于在Gotthard Base隧道（瑞士）和在靠近富尔达（德国）的“ Neuer Schluchterner隧道”的建设过程中获得的两个不同的隧道数据集。

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