Digitally Structured Subsurface Borehole Dataset for Geological Characterisation and Data‐Driven Modelling at
CERN
Zahra Hajighasemi, Biao He, Zhen Liu, Aohui Ouyang, Vanessa Di Murro, John Andrew Osborne, Andrea Visentin, Zili Li ABSTRACT
Tunnel performance can be influenced by various factors, among which geological conditions near the tunnel alignment play a critical role. This dataset provides subsurface geological information from two underground tunnel sections at European Organization for Nuclear Research (CERN): the Super Proton Synchrotron (SPS) and the Large Hadron Collider (LHC). The dataset includes spatial and geological attributes such as latitude, longitude, altitude, depth, ground surface elevation, borehole identifier and material/geological descriptions. In total, 73 boreholes are documented. The dataset is unique in that it converts previously unstructured PDF borehole reports into a structured, machine‐readable format, specifically capturing subsurface geology near the CERN tunnels. The structured dataset supports subsurface geological characterisation and can be used as input for future geological modelling and data‐driven analyses. It can also be combined with independent hydrogeological or infrastructure‐monitoring data where available. While the number of boreholes is limited, future integration with additional boreholes will allow development of a more comprehensive benchmark dataset. In the original digitised records, depth values are reported to a numerical resolution of 0.01 m where available. In addition to the original boundary‐based records, a separate derived dataset with an approximately 1 m vertical sampling interval is provided for convenient visualisation and subsequent computational use.